Peter DeCaro · Executive capability & portfolio overview

From operating problemsto working AI products.

Operations leader. AI architect. Accountable product builder. Peter DeCaro brings more than 25 years of technology-enabled operations experience to the design and delivery of AI-enabled systems. His work connects business intent, product architecture, specialist AI execution, quality control and production verification.

The result is both a product and a repeatable way to deliver it. Vantage Product Labs turns requirements into bounded implementation, tests the behavior that matters, preserves the source and recovery path, and carries lessons from each build into the next.
Working product systemsMulti-model AI operations and airfare monitoring provide distinct architecture examples.
Reusable delivery frameworkOne control system connects requirements, implementation, review and release.
Evidence before completionSource identity, test results and observed behavior support delivery claims.
Recovery by designDurable state, release identity and rollback are explicit engineering responsibilities.
Independent challengeExternal model assessments and retained defects inform continuous improvement.
Accepted September 28, 2026 · Gemini / Magai

Independent AI Architecture Assessments

Peter DeCaro · AI Operations & Solutions Architect. Two accepted instruments, with their own baselines, evidence boundaries and comparability qualifications.

AI Systems Architecture Capability

95.00

Gemini · 10-Dimension Systems Architecture Assessment

Measures the architecture: how well the system is designed, governed, tested, recovered, and controlled.

Applied-AI Architecture Capability

99.50

Gemini · Informed 9-Dimension Applied-AI Architect Assessment

Measures the architect: how effectively complex objectives are translated into governed AI systems while human judgment and control remain authoritative.

Evidence-based assessments using Google Gemini. Not vendor certifications or endorsements.

These are architecture assessments, not product completion percentages, vendor certifications or production acceptance. The 95.00 result uses the ten-dimension HF03/T01 instrument; the 99.50 result is an informed, non-blind V3-rubric successor with partial comparability. V4.0 FINAL / REV006 remains the published operating authority. Reviewed V5 improvements do not, by themselves, establish installed runtime CURRENT or promote a product release.

01 · Results & evidence

What the work has produced.

The most useful evidence is the connection between a real problem, the system built to address it, and an observable result. These examples distinguish delivered behavior from historical test evidence and reusable framework controls.

Product + historical QA evidence

MySmartRouter

Problem: fragmented model access, context and provider behavior make AI work difficult to coordinate and inspect.

Preserved product baseline: a governed multi-model workspace with routing, state, integration boundaries and review controls. The V6 Pilot 01 continuation remains paused; its newer source/CI record is not a production release.

263 PASS / 0 FAILBanked deterministic QA result recorded in the assessment evidence. Applies to that tested baseline.
Explore the product architecture ↗
Product + historical QA evidence

MyFlightWatcher

Problem: airfare monitoring spans providers, recurring runs, changing fares and recoverable state.

Delivered system: scheduled monitoring with provider integration, persistence, alerting and recovery responsibilities.

PHP 7/7 · Python 5/5Recorded test evidence, alongside migration, integration, recovery and secret-boundary evidence.
Explore the monitoring architecture ↗
Live behavior verified · September 17, 2026

VPL website delivery

Problem: a requested web change is incomplete until visitors can see the correct production behavior.

Delivered result: two sequential Git-backed Hostinger updates redirected the case-studies landing page to the home page, then changed the destination to the clean site-root address.

Live redirect confirmedOrdinary and cache-busted browser checks confirmed the expected destination without displaying the old page.
View the public website ↗
Evidence boundary: recorded test counts describe frozen prior baselines; they do not certify every later release. The website deployment was independently checked in the browser and against live HTML. Its automated GitHub verification encountered HTTP 403, so workflow success is not claimed. No new revenue, adoption or cost-saving metrics are inferred from these engineering results.
02 · The framework is a deliverable

A reusable operating system for delivery.

The VPL framework has progressed from V3 product evidence to V4 REV006 control requirements and the reviewed V5 HF03/T01 improvements. The inherited obligations remain intact except for the explicitly adopted A01/M26 persistence design. Peter’s contribution is the design of decisions, boundaries and acceptance rules—not simply the production of more code.

From engineering control to practical delivery value
Delivered framework capabilityPractical valueCompletion evidence
Bootstrap and current authority
Restore the current source, environment and build stage.
A resumed session can continue from an explicit state, with stale instructions identified.Current directive, source identity, readiness result and durable return route.
Bounded implementation and specialist roles
Separate building, testing, reviewing and release decisions.
Work has an owner, a defined boundary and an acceptance condition.Changed scope, executor return, QA output, independent review and adjudication.
Real integration and recovery gates
Test provider, database, state and failure behavior where applicable.
Completion depends on the actual dependency and recovery path, beyond a successful local demonstration.Contract results, negative-path tests, migration checks and rollback proof.
Controlled production delivery
Governed Git source, explicit Hostinger target and live validation.
A change can be traced from reviewed source to observed public behavior.Commit or release identity, deployment result, ordinary and cache-aware checks, last-known-good reference.
Evidence banking and framework improvement
Retain findings and promote verified general lessons.
Each build leaves reusable knowledge and a reviewable record for the next product.Raw results, defect disposition, decision history and versioned control changes.
Defined capability versus proven execution: the framework preserves 14 historical Cursor agent contracts. V4 maps each role to GPT Scheduled Tasks and migrates them one at a time. Historical contract completion is separate from GPT creation, runtime verification and activation.
Inherited V4 REV006 foundation · reviewed V5 continuation

Build on accepted work. Prove each next step.

The inherited V4 foundation supplies the reviewed V5 continuation with three evidence-selected build paths, seven progressive checkpoints, two-baseline regression comparison, earlier owner feedback and independent-model challenge throughout material work. The intent is faster learning without losing what already works.

Choose the right execution path

Manual, Light Build and MySmartRouter are peer options. Select by actual capability, risk and coordination value; escalate only the component that needs it.

Make preservation measurable

Compare each candidate with both the last accepted checkpoint and the locked baseline. Keep requirements, visual behavior, source identity and untested cases visible.

Connect tools to accountable outcomes

Git and Drive, Hostinger API and Git deployment, membership, payment and marketing each have a defined responsibility, proof requirement and recovery boundary.

FINAL identifies owner-directed operating guidelines. Historical assessments retain their original scope and dates; the accepted V5 scoped reviews and each product’s runtime acceptance remain distinct.

Explore the V4 checkpoint and regression controls ↗

Framework lineage, development effort and continuous improvement

The work progressed from product architecture and historical assessment into reusable delivery controls, source-bound review, finding reconciliation, bounded repairs, replay and final assessment. The estimated cumulative investment is 240–400 engineering-hours: a low-confidence, unlogged reconstruction of the framework and its review/evidence system, not a timesheet.

The six workstream ranges below sum to that single cumulative estimate. Each is placed beside the milestone that illustrates it; it is not the measured duration of that milestone or an additional per-version allowance. Peter’s personal hours, model processing time, elapsed time, savings, billable effort, future product completion and live deployment are not measured by this estimate.

01 · V3 foundation · September 6, 2026

Frame the problem and retain human control.

The original nine-dimension V3 assessment recorded 97.00. Product evidence linked decomposition, architecture, orchestration, integration and recovery to human decisions. That result and its original classification remain historical, not today’s headline.

40–64 estimated hours, cumulative workstream: architecture framing, authority and human decision boundaries.

02 · V4.0 FINAL / REV006 foundation

Turn delivery lessons into reusable controls.

Bootstrap-first authority, three peer build paths, seven checkpoints, independent review and two-baseline preservation made accepted behavior the reference for later work. Workflow, source identity, persistence and rollback became explicit responsibilities rather than assumptions.

48–80 estimated hours, cumulative workstream: workflow, persistence, source identity and recovery design.

03 · MySmartRouter V6 candidate · September 25

Test the boundary before trusting a signed result.

The R69 record preserved all 291 R59 baseline blobs and added 52 pilot/workflow files. A failing-first gate preceded a corrected exact-head CI run with 16/16 pilot cases. Prepared delivery artifacts did not prove provider receipt, installed runtime CURRENT or independent acceptance of the new head.

40–72 estimated hours, cumulative workstream: verification tooling, fixtures and negative-path tests. This illustrates verification practice; it is not a separate MySmartRouter completion estimate.

04 · V5 HF03/T01 review and reconciliation

Separate findings from repetition, then repair narrowly.

The 451-file frozen candidate remained intact while independent reviews challenged inheritance, timeout assumptions and evidence provenance. Action 4’s exact-payload gate retained 210 mixed records in 106 content/reference groups; repeated content did not become extra defects or votes. Forty-two focused tests supported that sidecar’s scoped acceptance.

56–88 estimated hours, cumulative workstream: independent review, triage and bounded remediation, including failed attempts and repeated verification.

05 · September 28 · decisions, replay and evidence

Make the correction and its limits inspectable.

Action 1 mapped 328 spans across 35 sources. The adopted A01/M26 amendment made persistence proof invocation-bound; the complete inherited GL-15 safeguards remained verbatim. The semantic disposition was ADEQUATE_WITH_STATED_LIMITATIONS at specification level. Action 3 used 13 focused tests and a non-blind Linux replay of a test-only subprocess proxy. Neither action established unmocked Guard 2 or production recovery.

32–56 estimated hours, cumulative workstream: evidence packaging, provenance and reproducibility.

06 · September 28 · accepted successor assessments

Assess the evidence without merging the instruments.

Gemini/Magai issued 95.00 for AI Systems Architecture Capability and 99.50 for Applied-AI Architecture Capability. The gains were +0.50 against the frozen HF03/T01 baseline and +2.50 against the historical V3 result. The latter remains informed/non-blind with partial comparability. Report R02 corrected rationale without changing either score.

24–40 estimated hours, cumulative workstream: technical documentation and assessment preparation.

Continuous improvement: develop → challenge independently → reconcile findings → make a bounded correction → test and replay → preserve the evidence and limitations → assess → carry validated lessons into the next authorized step. The historical September 23 94.50 table and the frozen HF03/T01 94.50 baseline keep their own dimension identities; the shared total does not make them interchangeable.

Next boundary: MySmartRouter remains paused at its accepted checkpoint. Any continuation, exact-head independent acceptance, runtime verification or production change requires its own authorization and evidence. Publishing this overview neither restarts that product nor promotes V5 to operating authority.

PythonLinuxMagaiGitHub Actions
03 · Judgment in execution

Choose the delivery path the problem needs.

The framework formalizes three build paths. The architectural decision is to use the smallest path that can satisfy the requirements and verification burden, then escalate only the component that needs more coordination.

Light Build

Direct, contained delivery

GPT/Codex works directly on bounded applications and artifacts when it can access, implement, test and package the work. Keeps handoffs proportionate to the task.

Manual

Directed specialist work

GPT leads a deliberate build and review cadence with calibrated human or specialist executors. Useful where environment access or technical scope requires an explicit handoff.

MySmartRouter

Coordinated orchestration

A governed path for work whose multiple stages and resources justify additional coordination. Shared authority, evidence and release controls remain part of the design.

What this means for an employer, client or partner: Peter connects operating priorities to implementation choices, defines what “done” must prove, directs specialist execution, challenges results and turns recurring failure patterns into better controls. Delivery economics are a design criterion; measured business impact must still be established for each engagement.

Results-focused revision · September 28, 2026 · V4.0 FINAL / REV006 remains the published foundation. The reviewed V5 HF03/T01 continuation and accepted 95.00 / 99.50 assessments are described with their scope limits. September 23 REV007 R4/HOLD references are historical positions, not a current restart directive. Supporting assessment records retain their original dates and constructs.

Human Foundation

The operating career came first. The AI framework is the translation.

This scorecard evaluates AI architecture, but the architecture is informed by a much older discipline: how to make complex organizations, workflows, technology and people perform under measurable control. Peter's career has consistently sat at that intersection.

01

Operational architecture before AI

Across senior operations, customer success, service delivery, business-process and transformation roles, Peter has worked with scaled teams, outsourced and offshore operations, SaaS and subscription environments, CRM/ERP platforms, executive KPI systems and rapid-growth organizations. The recurring work was architectural: clarify the operating model, remove friction, establish ownership, create measurable flow and make the system repeatable.

02

Six Sigma turned improvement into control

Lean Six Sigma and Black Belt training reinforced a principle that now governs Vantage Product Labs: improvement is incomplete until it is measurable and controlled. DMAIC therefore governs the assessment process itself. Every architecture claim must survive evidence, independent evaluation, comparison against external best practice and a recurring control cycle that can expose regression as readily as improvement.

03

AI changes execution, not accountability

Frontier models can now perform portions of coding, research, testing, design and analysis that once required larger specialist teams. Peter's architectural role is not to imitate every specialist. It is to define the business problem, select and coordinate specialist capabilities, impose interfaces and constraints, protect state and source authority, challenge outputs, make tradeoffs and remain accountable for the resulting product.

The thesis being tested: if the same human architectural fingerprints—governance, modularity, evidence gates, state discipline, independent review, economic pragmatism and controlled iteration—appear repeatedly across different products and become more sophisticated over time, that is meaningful evidence that the human architect is materially shaping the quality of AI-assisted outcomes.
Career → Control Lineage

The architecture method is an operating system translated from 25+ years of technology-based operations.

The framework is not a developer methodology with operations language added later. Its recurring controls reflect the work Peter has repeatedly performed in SaaS, digital advertising, customer operations, BPO/offshore delivery, revenue operations and enterprise systems: define the operating model, make performance visible, govern handoffs, standardize the critical path, detect variance early and keep improving without losing control.

The public hypothesis is testable: the stronger and more consistent these operating patterns appear in independent AI-system evidence, the stronger the case that human architectural judgment—not model capability alone—is shaping the outcome.

Operating experienceDocumented career evidenceHow it informs the AI frameworkWhat the assessment must test
SaaS / technology operationsOperating models, unit economics, WBR/QBR cadence, customer-success and revenue alignment, CRM/ERP integration.Architecture must connect technical design to operating economics, measurable outcomes, ownership and scale.Whether systems are commercially bounded and operationally coherent—not merely technically interesting.
Digital advertising operationsAI/no-code workflows and performance tracking across 24+ PPC campaigns; high-volume, feedback-sensitive execution.Short feedback loops, measurable conversion/throughput, controlled experimentation and fast correction.Whether the lab learns from observed results and changes architecture when evidence changes.
BPO / offshore operationsOutsourcing redesign, standardized processes, performance frameworks, cross-team handoffs and offshore scale.Modular delegation: clear responsibilities, explicit interfaces, acceptance criteria, escalation and independent review.Whether AI agents/models are governed like accountable specialist workstreams rather than trusted as opaque generalists.
KPI governance & executive cadenceExecutive dashboards, SLA health, leading/lagging indicators, WBR/QBR reporting and decision support.Every architecture claim needs a metric, evidence source, threshold, owner and control response.Whether the scorecard produces actionable deltas rather than narrative praise.
ERP / CRM / workflow integrationNetSuite, HubSpot, Zendesk, Salesforce; cross-functional process redesign and automation.System boundaries, source authority, state, handoffs, data contracts and integration reliability are first-class architecture concerns.Whether implementation respects source-of-truth and interface discipline.
Lean Six Sigma / continuous improvementBlack/Green/Yellow Belt, root-cause analysis, Kaizen facilitation and 2023 continuous-improvement recognition.DMAIC governs the measurement system; Control is permanent and must expose regression, not validate ego.Whether negative findings close into corrective action and remain controlled in subsequent cycles.
Stage A contamination safeguard: this career lineage explains why the measurement system exists, but career prestige does not earn Stage A technical points. The architecture evidence is scored blind first. Professional background is introduced only after Stage A is frozen to interpret whether observed architectural patterns plausibly reflect durable human operating judgment.
Current accepted assessment · September 28, 2026

Applied-AI Architecture Capability

Gemini · Informed 9-Dimension Applied-AI Architect Assessment

Measures the architect: how effectively complex objectives are translated into governed AI systems while human judgment and control remain authoritative.

The successor adds evidence of source-bound preservation, an explicit owner decision, repeatable local recovery and mechanical grouping of multi-model outputs. It assesses demonstrated applied-AI architectural capability; it does not certify product deployment or unconstrained automation.

99.50CURRENT INFORMED SUCCESSORHistorical V3 baseline: 97.00 · gain: +2.50 points
Partial comparability. This is not a new blind product rerun. The historical Principal classification below belongs to the original V3 assessment.

The nine scored dimensions

15/15Problem Decomposition & Operational Translation
15/15AI / System Architecture
15/15Human Architectural Judgment & Control
12/12AI Agent Orchestration & Leverage
9.5/10Product / System Integration
10/10Workflow, State & Recovery Architecture
10/10QA / Validation / Debugging Architecture
8/8Governance & Change Control
5/5Scope Expansion via AI

Why the distinction matters

The V3 rubric gives different weights to nine aspects of capability. New framework evidence was admitted in dimensions A–D and F. Product integration remained unchanged because this round did not add product-integration design evidence—not because production deployment carries scoring weight.

Human judgment remains central: defining requirements, choosing the persistence policy, protecting inherited safeguards and accepting only supported results.

GeminiClaudePython
Historical September 6 V3 assessment · 97.00 · original Principal classification and evidence
Independent assessment by eight independent model families: OpenAI GPT-5.6 Sol · Anthropic Claude Opus 5 · Google Gemini 3.8 Flash · xAI Grok 4.6 · DeepSeek · Mistral · Moonshot Kimi · Zhipu GLM 5.297.0/100 · Principal Applied-AI Architect
Historical independent assessment · September 6, 2026

97.0/100. Principal Applied-AI Architect.

Google Gemini's independent V3 evaluation placed Peter DeCaro at the top of the Principal Applied-AI Architect band. In that historical report, this was the master-level finding: repeated, system-level architectural judgment across materially different products; disciplined human control of AI engineering; sophisticated orchestration; deterministic QA; source authority; recovery thinking; and governance that converts AI speed into controlled engineering output.

93OpenAI / GPT-5.6 SolPrincipal Applied-AI Architect
90xAI / Grok 4.6Expert
85Zhipu / GLM 5.2Expert
M4Anthropic / Claude Opus 5Highly Mature / Governed AI Engineering Framework
97.0 HISTORICAL V3 RESULT · SEPTEMBER 6 Google Gemini · Principal Applied-AI Architect
Independent model assessment using Google Gemini; this is not a Google-issued certification, employment credential, or official Google endorsement.

Why the historical V3 assessment recorded 97.00

Gemini's V3 score was built dimension-by-dimension under a frozen applied-AI architecture construct rather than from deployment status, enterprise scale or production certification.

14.5/15Problem Decomposition & Operational Translation
14.5/15AI / System Architecture
14.5/15Human Architectural Judgment & Control
11.5/12AI Agent Orchestration & Leverage
9.5/10Product / System Integration
9.5/10Workflow, State & Recovery Architecture
10/10QA / Validation / Debugging Architecture
8/8Governance & Change Control
5/5Scope Expansion via AI

What the assessment adds to the delivery evidence

The preserved model evaluations provide an external perspective on Peter’s architectural judgment: problem decomposition, human direction of AI, integration, state, recovery, QA and governance. Their value is strongest when read alongside the source, test results and product behavior. They are supporting assessments, not vendor-issued credentials or a substitute for release acceptance.

Technical evidence behind the narrative: MySmartRouter banked 263 PASS / 0 FAIL across its deterministic QA harness; MyFlightWatcher banked PHP 7/7 and Python 5/5 tests plus migration, recovery, secret-boundary and integration evidence. Both products preserve exact source identities and V2/V3 remediation lineage.

Trade ShowsBusiness Cards / QR Portfolio LinkedIn Profile ↗ Executive / Architecture InterviewsClient & Partner Reviews

Independent model ecosystem — all evaluator families represented

Historical V3 result: Gemini 97.00 · September 6, 2026Historical eight-family program; the preserved numeric returns retain their original dates and distinct constructs.

Eight model families were represented in the historical assessment program. Numeric results shown here are the preserved V3 returns identified on this page; framework-maturity findings and unavailable numeric results remain separate. They do not imply eight new scores or eight reviews of the September 28 successor evidence.

OpenAI / GPT Anthropic / Claude Google / Gemini xAI / Grok DeepSeek Mistral Moonshot / Kimi Zhipu / GLM

Scoring authority stack — demanding standards with real industry standing

These authorities were selected because they are not marketing scorecards. They are used by architects, security teams, regulated enterprises, government programs, software suppliers and engineering organizations to challenge systems on governance, risk, architecture quality, trust boundaries, provenance and software-supply-chain discipline. Their value here is precisely that the standards are difficult: they force evidence, traceability, explicit controls and defensible engineering judgment.

NIST

NIST AI RMF + GenAI Profile

National Institute of Standards and Technology guidance used across U.S. government, regulated industries, enterprises and technology vendors to manage AI risk through governance, measurement and operational controls.

Tough standard: risk must be mapped, measured, governed and evidenced—not merely described.
ISO

ISO/IEC 42001

The international AI management-system standard used by organizations that need auditable controls around AI policy, accountability, risk and continual improvement.

Tough standard: evidence of ownership, repeatability and continual improvement is expected.
42010

ISO/IEC/IEEE 42010

A foundational architecture-description standard built around stakeholders, concerns, viewpoints, views and rationale. Used to make architecture reviewable rather than merely visual.

Tough standard: decisions must connect to stakeholder concerns and architectural rationale.
SEI

SEI ATAM / Quality Attributes

Carnegie Mellon Software Engineering Institute methods evaluate architecture tradeoffs against reliability, maintainability, security, performance and modifiability.

Tough standard: it actively searches for risk, sensitivity points and tradeoffs.

OWASP GenAI / ASVS / SAMM

Industry-standard application and AI security guidance used by engineering, AppSec, penetration-testing and enterprise security teams.

Tough standard: concrete attack surfaces, negative paths and trust boundaries must survive scrutiny.
MITRE

MITRE ATLAS

MITRE's knowledge base for adversarial threats to AI-enabled systems, used by security researchers, defenders and organizations testing realistic AI attack behaviors.

Tough standard: asks whether the architecture remains defensible under active adversarial pressure.
CSA

Cloud Security Alliance AI Controls

CSA control frameworks are used by cloud-security and enterprise-risk teams to translate expectations into operational controls.

Tough standard: controls must be explicit, assigned and testable across the actual cloud boundary.
OSSF

OpenSSF Scorecard

Used across open-source and software-supply-chain programs to assess repository practices that affect integrity, dependency safety and change-control confidence.

Tough standard: repository and build discipline must be observable, not asserted.
SLSA

SLSA Provenance

A software-supply-chain framework focused on build provenance and artifact integrity where organizations need confidence that reviewed source is the source actually built and delivered.

Tough standard: provenance must be traceable through source, build and artifact identity.
Historical method context: these references informed the earlier review’s questions about traceability, trade-offs, negative-path testing and control. They are not certifying bodies for the scores on this page. The accepted September 28 results must be read against their two identified instruments and the supplied evidence, not inferred from the authority logos.
Historical methodology-development record · earlier v1.5 program
Scorecard Test Established
Method validation completed before scoring. Independent model reviewers challenged the rubric, evidence rules, attribution logic, contamination controls and scoring precision. Accepted changes were incorporated before candidate scoring, creating a tougher and more credible measurement system.

The scoring method was challenged before the architecture was scored.

Before candidate scoring, the assessment method itself was subjected to independent challenge. The purpose was to detect construct drift, prior-score anchoring, evaluator contamination, evidence-access failure, human-attribution ambiguity, false precision, conflicting scoring authorities, and criteria that did not belong in an applied-AI architecture capability test. Accepted corrections were incorporated before the scoring authority was frozen.

Why multiple model families?Different model families have different training priors, reasoning tendencies, tolerance for ambiguity and evaluation habits. A multi-family panel reduces dependence on any one vendor's framing and makes convergence more meaningful.
Why blind evaluator isolation?Each evaluator was prevented from reading sibling results or prior numeric scores. This limits anchoring, consensus imitation and cross-model score contamination.
Why freeze the evidence?MySmartRouter and MyFlightWatcher were tied to exact source identities and frozen evidence packages. Every evaluator therefore judged the same technical state instead of a moving target.
Why preserve negative evidence?Known defects, access limitations and remediation history were retained so the process could distinguish resolved weaknesses from current weaknesses rather than presenting only favorable material.
Why one scoring authority?Each evaluation iteration must expose exactly one operative scoring authority. Superseded weights, legacy dimensions, prior output schemas and conflicting evaluation instructions are removed from the evaluator entry path or explicitly marked non-operative before freeze.
Why challenge the test first?A high result is more persuasive when the measurement system is designed to resist inflated scoring. The methodology review was intended to make the score harder to earn, not easier to market.
Why this matters: the architecture was not allowed to benefit from an untested scorecard. The measurement system was first challenged for bias, contamination, stale authority, over-weighted production/security criteria, evidence-access defects and attribution errors. Only after those weaknesses were corrected was the architecture judged. That separation strengthens the evidentiary value of the final results because the process tested the test before trusting the score.
Method Review 01 · Complete
A

Anthropic / Claude

Independent methodology red-team

What was reviewed: construct validity, Human Architectural Agency, Stage A contamination, evidence selection, five-model panel design, authority use, negative evidence, falsifiability and public-credibility risk.

What changed: the revised method now discloses evaluator/evidence entanglement, replaces impossible candidate anonymity with narrative/outcome blindness, requires primary human-decision evidence, freezes negative evidence before positive exemplars, enforces execution citations and contradiction handling, narrows the authority layer, and strengthens null-result/falsification controls.

Method review result: READY TO FREEZE AFTER MUST CHANGES
Candidate A scoring: NOT PERFORMED
Method Review 02 · Complete
xAI

xAI / Grok

Second de novo methodology red-team — complete

Independence control: Grok receives the revised pre-freeze method but not Claude's raw recommendation list before its own review is frozen. This reduces anchoring and consensus pressure.

What it must challenge: Grok independently confirmed that the method still required targeted changes before freeze: consolidate overlapping rubric dimensions, hard-gate HAA on Attribution Confidence, reduce attribution weight for uncorroborated retrospective/AI-drafted decision records, require at least three less-entangled runs for any official subset synthesis, and move full scoring to a quarterly cadence.

Method review result: READY TO FREEZE AFTER MUST CHANGES
Candidate A scoring: PROHIBITED
Method Review 04 · CompleteDeepSeek

Fourth de novo methodology red-team — complete

Result: READY TO FREEZE AFTER MUST CHANGES. The accepted new control distinguishes platform-native chronology from verified human-origin agency so agent-authored commits/logs cannot self-certify HAA.

Candidate A scoring: NOT PERFORMED

Method Review 05 · CompleteOpenAI

Fifth de novo methodology red-team — complete

Result: READY TO FREEZE AFTER MUST CHANGES. OpenAI identified control-plane inconsistencies: stale rubric/schema language, five-vs-eight evidence scope ambiguity, unstructured HAA output, negative-evidence manifest enforcement and access-dry-run requirements. Those controls are reconciled in v1.5.

Candidate A scoring: NOT PERFORMED

Why this matters: the scorecard is not treated as a self-validating rubric. External model criticism is preserved, dispositioned and used to refine the measurement system before formal freeze. All five reviewer families completed methodology-only review. Accepted MUST changes are reconciled in v1.5; the remaining gate at that historical checkpoint was model/evidence/hash/access freeze before Candidate A scoring. A recommendation is adopted only when it improves validity, fairness, reproducibility, attribution or credibility; disagreement and rejected recommendations remain part of the audit trail.
Current accepted ten-dimension result · September 28, 2026

AI Systems Architecture Capability

Gemini · 10-Dimension Systems Architecture Assessment

Ten dimensions. One equal mean.

Measures the architecture: how well the system is designed, governed, tested, recovered, and controlled.

HF03/T01 frozen baseline: 94.50 · gain: +0.50 points. The score is the equal mean of ten dimensions.

95.00September 28, 2026
DimensionBaselineSuccessorChange
AI / System Architecture9596+1
Problem Decomposition9596+1
Multi-Model Orchestration9495+1
Product / System Integration92920
API / Data / Tool Integration92920
Workflow & State Architecture9596+1
Governance & Change Control9394+1
QA / Debugging / Recovery97970
Operational Problem Translation95950
Individual AI Leverage97970

Source-bound specification acceptance, local timeout recovery and exact-payload grouping support the delta. Unmocked Guard 2 and runtime CURRENT were not established. The frozen 94.50 result and the earlier September 23 assessment are not rewritten.

Historical September 23 assessment · 94.50 · original Expert classification and dimension table
Historical Independent Assessment · September 23, 2026

Google Gemini assessment: 94.5/100 · Expert.

A controlled delta assessment against Gemini’s September 1 baseline of 93.75 found a net +0.75 movement. The assessment preserved the original ten dimensions and was allowed to raise, lower or hold each score. Product / System Integration decreased by one point while Multi-Model Orchestration and QA / Debugging / Recovery increased by two points each.

Gemini controlled delta assessment

94.50 / 100

Classification: Expert · Baseline: 93.75 / 100 · Delta: +0.75

94.5/100Last updated: 9-23-26
EvaluatorGoogle Gemini
DateSeptember 23, 2026
EvidenceV4 + MySmartRouter V6 delta
Truth boundaryArchitecture ≠ runtime certification
Capability dimensionSeptember 1 → September 23Δ
AI / System Architecture
94
95
+1
Problem Decomposition
95
95
0
Multi-Model Orchestration
92
94
+2
Product / System Integration
93
92
-1
API / Data / Tool Integration
92
92
0
Workflow & State Architecture
94
95
+1
Governance & Change Control
96
97
+1
QA / Debugging / Recovery
91
93
+2
Operational Problem Translation
95
95
0
Individual AI Leverage
97
97
0
Overall assessment93.75 → 94.50+0.75
Historical evidence boundary · September 23: Gemini retained the then-unverified or blocked Hostinger/API integration evidence and criticism of the proposed persistence/verifier design. REV007 R4 was a candidate at that checkpoint. The reviewed V5 evidence does not retroactively change this historical report or establish product-wide production acceptance.
Historical V3 multi-model scorecard · original results, not September 28 scores
Historical Independent Architecture Validation

Preserved V3 capability results remain part of the evidence record.

Google Gemini returned 97.0/100 — Principal Applied-AI Architect. OpenAI independently returned 93/100 Principal; xAI Grok returned 90/100 Expert; GLM returned 85/100 Expert. Claude independently rated the VPL Build Framework M4 — Highly Mature / Governed AI Engineering.

91.3Top-Level Numeric V3 Capability MeanMean of the four preserved numeric V3 capability returns: 97, 93, 90 and 85. Claude's M4 framework finding is shown separately because it measures framework maturity, not the same numeric capability construct.
Google / GeminiIndependent V3 architecture capability evaluation97.0/100 · Principal
OpenAI / GPT-5.6 SolIndependent V3 architecture capability evaluation93/100 · Principal
xAI / Grok 4.6Independent V3 architecture capability evaluation90/100 · Expert
Zhipu / GLM 5.2Independent V3 architecture capability evaluation85/100 · Expert
Anthropic / Claude Opus 5Framework maturity — separate constructM4/5 · Highly Mature
The 91.3 roll-up is a descriptive mean of the four preserved numeric V3 returns, not an additional evaluator-issued score. Claude’s framework-maturity result uses a separate construct. DeepSeek, Mistral and Kimi remain part of the historical program record without implying a numeric result or a new September 28 assessment.
97GeminiPrincipal Applied-AI Architect · historical V3 classification.
93OpenAIPrincipal Applied-AI Architect · independent corroboration.
90GrokExpert · independent corroboration.
85GLMExpert · additional independent confirmation.
Historical V2.0 · V1 improvement requirements

The first assessment cycle becomes a stronger operating process.

V2.0 preserves the complete V1 five-model baseline and converts the recurring evaluator findings into explicit process requirements for the next controlled build cycle. The emphasis is not on rewriting the architecture or pursuing a target score. It is on making implementation, QA, recovery, governance, evidence access and human decision provenance directly inspectable.

01 · Architecture Traceability

Connect architecture decisions to source and runtime behavior.

For MySmartRouter, maintain a source-linked architecture map that ties each material boundary and decision to the responsible module/path, runtime responsibility, quality attribute and failure/recovery path.

  • Source-linked architecture map
  • ADRs for material tradeoffs
  • Runtime responsibility and recovery mapping
02 · Acceptance Evidence

Make decomposition outcomes directly verifiable.

Preserve the existing problem-decomposition discipline while linking each material problem statement and constraint to the authorized wave, acceptance gate and observed result.

  • Problem → constraint → wave lineage
  • Acceptance matrices
  • Observed outcomes rather than forecast claims
03 · Orchestration Evidence

Persist the multi-model control loop as an event chain.

For every governed build wave, retain machine-readable records of orchestrator, executor, reviewer, model/provider route, retries, gate outcomes, adjudication and final bank/release state.

  • Correlation and directive IDs
  • Provider/model metadata
  • Reroute and review-independence history
04 · Product Integration Proof

Use MySmartRouter as the flagship implementation proof chain.

Tie the exact source tree and deployed version to the end-to-end workflow, persistence, provider calls, UI/runtime behavior, error handling, health evidence and deployment record.

  • Exact deployed source identity
  • End-to-end runtime proof
  • Deployment and health evidence
05 · API / Data / Tool Evidence

Show provider and data behavior, including failure.

Capture request/response contracts, provider failover, quota behavior, persistence records, schema/migration evidence, security configuration and at least one controlled provider failure/recovery trace.

  • API traces and contracts
  • Schema/migration evidence
  • Failover and quota telemetry
06 · Workflow / State Recovery

Test rehydration and recovery across real state transitions.

Preserve tests for cold rehydration, interrupted execution, stale lease/lock behavior, idempotent retry, failed delivery, recovery and rollback-as-new-version behavior.

  • Rehydration tests
  • Fault injection and recovery traces
  • Before/after state snapshots
07 · Governance Authority

Expose one operative evaluator authority at package freeze.

Maintain one authoritative evaluator instruction set, detect superseded or contradictory files automatically, and enforce source-version identity and authority precedence in the evidence bank.

  • Authority manifest
  • Conflict scan
  • Freeze gate and version lineage
08 · QA / Debugging / Recovery

Bank raw evidence, not only QA summaries.

For every material release, preserve raw test output, defect records, failed regression examples, incident/recovery evidence, CI results, security scans, rollback proof and post-deploy health evidence.

  • Regression and defect ledger
  • CI/static/security outputs
  • Rollback and recovery drills
09 · Evidence Maturity

Separate portfolio breadth from production maturity.

Retain broad portfolio evidence as architecture proof while attaching production claims only to capabilities with explicit maturity-state evidence.

  • SPECIFIED → IMPLEMENTED_SOURCE → BUILD_VERIFIED
  • RUNTIME_VERIFIED → PRODUCTION_VERIFIED
  • BANKED_WITH_REGRESSION_EVIDENCE
10 · Human Architectural Agency

Capture material human decisions without restoring routine human routing.

Keep routine NEXT/REVIEW autonomous. Capture only material human-origin decisions such as rejected recommendations, overridden model choices, scope vetoes, architectural constraints, commercial assumptions, destructive-action approvals and post-failure changes in direction.

  • Timestamped decision provenance
  • Prior agent proposal retained
  • Rejection, override and scope-veto evidence
11 · Evaluator Access

Provide the same evidence in integrity and inspection formats.

Ship a frozen ZIP for integrity together with a complete pre-extracted folder tree for inspection. Run a platform dry-run before scoring and require hash/equivalence records for any controlled transformation.

  • Frozen ZIP + full unzipped tree
  • Platform access dry-run
  • Transformation hash/equivalence record
Historical V2.0 process position: the architecture baseline remains preserved. The improvement cycle strengthens how the work is traced, tested, evidenced, recovered, governed and independently inspected before the next assessment cycle.
Historical evaluator-family results · not additional current assessments
Eight-Model Independent Assessment Program

Multiple independent model families support the same architecture narrative.

The historical program included OpenAI, Anthropic/Claude, Google/Gemini, xAI/Grok, DeepSeek, Mistral, Kimi and GLM. These identities describe the earlier review ecosystem. The September 28 successors were issued by Gemini through Magai, with their own source bindings and limitations; historical participation is not fresh corroboration of those scores.

OpenAI
AIAnthropic / Claude
Google / Gemini
xAI / Grok
DSDeepSeek
Mistral
Moonshot / Kimi
GLMZhipu / GLM
Historical V3 result: 97.00 · original Principal Applied-AI Architect classificationOther preserved returns retain their original dates, methods and evidence limits. They are not averaged into either current score.
97

Google Gemini

Principal Applied-AI Architect. Preserved September 6 V3 result; not either September 28 successor assessment.

93

OpenAI GPT-5.6 Sol

Principal Applied-AI Architect. Independent principal-band confirmation.

90

xAI Grok

Expert. Independent expert-level validation.

85

GLM / Zhipu AI

Expert. Additional expert classification.

M4

Anthropic Claude

Highly Mature / Governed AI Engineering. Independent VPL Build Framework maturity finding.

8

Evaluator Families

OpenAI · Claude · Gemini · Grok · DeepSeek · Mistral · Kimi · GLM.

Assessment methods · current instruments and preserved history

Two instruments. Two different questions.

The 95.00 systems-architecture score uses the equal mean of ten dimensions. The 99.50 informed applied-AI architect score sums nine weighted dimension awards. The methods below are bound to the September 28 evidence; the earlier v1.5 table remains a separate historical record.

View Scoring Rubric · both September 28 assessment instruments

Scoring Rubrics & Method Boundaries

The two scores answer related questions under different instruments. They must not be blended, averaged together or represented as a percentile.

Track 1 · ten-dimension HF03/T01 method

Each dimension is scored from 0 to 100. The final result is the arithmetic mean of all ten dimension scores. The September 28 comparison uses the frozen HF03/T01 identities below; the historical September 23 page retains its original labels and values.

DimensionScale / weight
1. AI / System Architecture0–100; equal 1/10 weight
2. Problem Decomposition0–100; equal 1/10 weight
3. Multi-Model Orchestration0–100; equal 1/10 weight
4. Product / System Integration0–100; equal 1/10 weight
5. API / Data / Tool Integration0–100; equal 1/10 weight
6. Workflow & State Architecture0–100; equal 1/10 weight
7. Governance & Change Control0–100; equal 1/10 weight
8. QA / Debugging / Recovery0–100; equal 1/10 weight
9. Operational Problem Translation0–100; equal 1/10 weight
10. Individual AI Leverage0–100; equal 1/10 weight

This is a documented comparison method, not a claim that an unavailable earlier detailed scoring manual was recovered. View the frozen-baseline comparison.

Track 2 · V3 applied-AI architecture rubric

The nine dimension ceilings sum to 100. Awarded points are summed directly; they are not averaged again. Production deployment absence has zero direct scoring weight. Framework maturity is a separate result.

DimensionMaximum points
A. Problem Decomposition & Operational Translation15
B. AI / System Architecture15
C. Human Architectural Judgment & Control15
D. AI Agent Orchestration & Leverage12
E. Product / System Integration10
F. Workflow, State & Recovery Architecture10
G. QA / Validation / Debugging Architecture10
H. Governance & Change Control8
I. Scope Expansion via AI5

The September 28 use is an informed, non-blind successor with partial comparability, not the original blinded product rerun. Read the complete preserved V3 authority and preserved historical Gemini result.

Complete preserved V3 scoring authority
VANTAGE PRODUCT LABS — ASSESSMENT V3
COMMON CALIBRATION AND SCORING AUTHORITY

Status: LOCKED FOR V3 RERUN
Purpose: Measure Peter DeCaro's demonstrated applied-AI architecture capability using a construct aligned to human-directed AI product engineering. This document supersedes V2 scoring interpretation for the V3 rerun only. V2 raw evaluator returns remain historical evidence and are not scoring authority for V3.

1. PRIMARY QUESTION

Based solely on the supplied evidence, assess Peter DeCaro's demonstrated capability to architect, direct, govern, validate, and improve AI-enabled product engineering systems. Evaluate his ability to translate ambiguous product objectives into technical architecture, make and enforce architectural decisions, direct AI agents across implementation and remediation, integrate systems/data/tools, design workflow/state/recovery controls, create validation and QA architecture, govern change, and use AI to expand engineering scope while retaining human control of architecture and acceptance.

The objective is the strongest defensible capability score supported by evidence. Do not optimize toward a target score, and do not import criteria outside this defined construct.

2. COMPARISON POPULATION

Compare against experienced independent product architects, technical founders, AI-enabled engineering leads, and boutique product-studio operators responsible for converting ambiguous ideas into working, governed software systems with substantial AI-assisted execution.

Do NOT compare against hyperscale platform architects, FAANG Distinguished Engineers, enterprise SRE organizations, regulated-industry compliance teams, or leaders whose principal differentiator is institutional scale. Enterprise-wide adoption, large-team management, global infrastructure scale, or organizational standardization are not prerequisites for high capability scores in this assessment.

3. V3 CAPABILITY BANDS

0–49: Developing
50–69: Proficient
70–84: Advanced
85–92: Expert
93–97: Principal Applied-AI Architect
98–100: Exceptional Demonstrated Capability

Principal in this assessment means unusually strong, repeated system-level architectural judgment, human-directed AI orchestration, integration, validation, recovery thinking, governance, and scope expansion across materially distinct products. It does NOT require enterprise-wide deployment or institutional adoption.

4. 100-POINT SCORING CONSTRUCT

A. Problem Decomposition & Operational Translation — 15 points
Assess ability to convert ambiguous goals, defects, constraints, and desired outcomes into bounded architecture, executable work packages, acceptance gates, and recovery paths.

B. AI / System Architecture — 15 points
Assess system decomposition, interfaces, trust/state boundaries, architectural coherence, tradeoff quality, extensibility, failure handling, and source-tied architecture decisions.

C. Human Architectural Judgment & Control — 15 points
Assess evidence that Peter defines objectives and boundaries, chooses among alternatives, constrains AI execution, rejects overreach, decides what is material, authorizes remediation, preserves source authority, and retains final acceptance control. HAA provenance is descriptive evidence supporting this dimension but HAA uncertainty must not impose an automatic score ceiling.

D. AI Agent Orchestration & Leverage — 12 points
Assess effective division of labor among GPT/Cursor/other models, independent review, bounded execution, model routing, context recovery, anti-drift behavior, parallelization, and the ability to use AI to multiply engineering throughput without surrendering architectural control.

E. Product / System Integration — 10 points
Assess demonstrated integration of application components, APIs, databases, tools, providers, workflows, interfaces, and heterogeneous technology stacks. Deployment status is not part of this dimension.

F. Workflow, State & Recovery Architecture — 10 points
Assess sessions, queues, leases, state transitions, retries, idempotency, persistence, rollback/recovery thinking, source identity, and continuity across agent/session boundaries.

G. QA / Validation / Debugging Architecture — 10 points
Assess whether the engineering system creates deterministic validation, negative-path testing, evidence banking, regression control, reproducibility, bounded repair, and systematic debugging. Production telemetry is not required for full credit.

H. Governance & Change Control — 8 points
Assess authority boundaries, immutable identities, ADRs, review separation, change disposition, anti-drift controls, rollback/banking, evidence discipline, and human approval boundaries.

I. Scope Expansion via AI — 5 points
Assess demonstrated ability to direct AI across multiple products/stacks/functions and materially expand the amount or breadth of engineering work one architect can govern.

TOTAL: 100 points.

5. ZERO-WEIGHT / NON-SCORING FACTORS

The following MUST NOT directly reduce any of the nine capability dimensions merely because they are absent, incomplete, or not demonstrated:

- production deployment status
- Hostinger deployment status
- DNS configuration
- production database operation
- production telemetry/observability
- live-provider execution
- live-user traffic
- production certification
- release readiness
- credential rotation as a deployment gate
- enterprise IAM/OAuth maturity unless the architecture task specifically required it
- SOC 2, ISO, PCI, HIPAA, or similar compliance posture unless explicitly part of the architecture problem
- hyperscale infrastructure
- multi-team institutional adoption
- enterprise organizational governance
- absence of production verification
- absence of external customer adoption

These items may be reported separately as Product/Release Readiness or limitations, but they carry ZERO direct weight in Peter's applied-AI architecture capability score.

6. SECURITY TREATMENT

Security is an architectural consideration, not a standalone weighted capability dimension in V3.

A security finding may affect the capability score ONLY when it directly demonstrates a material architectural judgment weakness in the scoped system—for example, a poorly designed trust boundary, unsafe secret propagation inherent in the architecture, or a failure mode caused by architectural design.

Do not deduct for generic compliance maturity, deployment credential rotation, lack of enterprise IAM, absence of production hardening, or security work that belongs to release readiness rather than architecture capability.

NO DOUBLE COUNTING: one underlying security issue may generate one localized capability deduction where directly relevant. The same underlying issue must not also be deducted across System Architecture, Integration, API/Data, State, Governance, and QA.

7. DEPLOYMENT / PRODUCTION TREATMENT

Deployment is outside the architecture-capability construct. Lack of production deployment, Hostinger validation, live DNS, production database evidence, production telemetry, live providers, or production traffic MUST NOT lower the capability score.

Production evidence may be used positively only when it directly demonstrates an architectural property. Conversely, a documented production failure may be used negatively only if it directly disproves an architectural claim. Mere absence of production evidence is neutral.

8. EVIDENCE AND NEGATIVE FINDINGS

Evaluate implementation evidence, source evidence, QA evidence, architectural records, decision history, bounded repairs, and negative evidence.

Do not award implementation credit for documentation alone when implementation evidence is required. However, do not treat documentation-only gaps, presentation gaps, missing production proof, or future-maturity suggestions as architectural defects.

Historical failures that were detected, correctly diagnosed, repaired, and banked should be evaluated as evidence of debugging/recovery capability rather than automatically counted as enduring negatives.

A resolved defect should not continue to reduce the final score unless its architectural effect remains present in the final evaluated state.

Recommendations for future maturity are not deductions unless they reveal a demonstrated current weakness inside the defined scoring construct.

9. V1→V2/V3 IMPROVEMENT CREDIT

Give explicit credit for demonstrated architectural improvement after adversarial or independent review, including:
- soliciting criticism
- separating legitimate findings from overreach
- prioritizing bounded repairs
- correcting architecture/process weaknesses
- validating repairs deterministically
- preserving negative evidence
- improving the engineering system itself

The ability to improve systems under independent review is a core applied-AI architecture capability.

10. HAA TREATMENT

Human Architectural Attribution is descriptive and evidentiary, not a separate penalty layer.

Use available evidence to assess Peter's role in framing, boundary-setting, option selection, constraint-setting, acceptance, rejection, remediation authorization, and final architectural disposition.

Do not lower the capability score solely because attribution metadata is incomplete or because AI authored implementation text/code. The capability being measured explicitly includes the ability to direct AI as an engineering workforce while retaining human architectural control.

11. VPL BUILD FRAMEWORK — SEPARATE MATURITY RESULT

Evaluate the VPL Build Framework separately from Peter's 0–100 score:
M1 — Ad Hoc
M2 — Structured
M3 — Controlled
M4 — Highly Mature / Governed AI Engineering
M5 — Institutionalized / Scaled

M5 is not required for Peter to receive Principal-level applied-AI architecture capability. Framework maturity is a separate result.

12. PRODUCT / RELEASE READINESS — SEPARATE NON-SCORING RESULT

If useful, report product/release readiness separately. It must not alter the 0–100 architecture capability score.

13. COMMON EVIDENCE SOURCE

For evidence, use the same frozen Assessment V2 evidence package previously used by the panel:
Wave_5_Parallel_Blind_Evaluation/00_COMMON_FROZEN_INPUT
Folder ID: 1rcjCJfOemADMz1VRaWNFOvuJFm7v33Bl

Use the exact final scoring identities already frozen for the two V2 target codebases:
MySmartRouter: 3ffa486e8fd8ba80dc8574638a770e84d88a1d5d
MyFlightWatcher: cfca92f02163350e79aff973e468bea0a4e99117

Primary product evidence for architecture improvement is MySmartRouter and MyFlightWatcher. The VPL Build Framework is evaluated separately. Other historical VPL products may support breadth, lineage, and scope-expansion evidence but must not create deductions because they were not V2 remediation targets.

14. BLINDNESS / SESSION RULES

Each model must run in a fresh independent session.
Each model reads this V3 common authority and the common frozen evidence source.
No model may read another model's V2 or V3 evaluator return.
Do not use prior numeric scores, prior evaluator conclusions, target-score language, forecasts, or cross-model synthesis.
Do not infer a desired score.
Do not modify the common evidence package or this authority.

15. REQUIRED EVALUATOR OUTPUT

Every evaluator must return:
- provider/model identity
- confirmation that this V3 authority was read before scoring
- confirmation of fresh-session/blindness compliance
- evidence-access status
- scope confirmation
- dimension-by-dimension score with rationale
- weighted total /100
- capability band
- Peter human architectural judgment/control assessment
- AI orchestration/leverage assessment
- VPL Build Framework maturity M1–M5
- material architectural limitations within the defined construct
- separate non-scoring product/release-readiness observations, if any
- confidence level
- explicit statement that no sibling evaluator output or prior score was used

16. SCORING INSTRUCTION

Score the strongest defensible demonstrated capability under THIS V3 construct.

Do not import enterprise-scale, production-readiness, deployment, security-compliance, SRE, institutional-adoption, or organizational-scale criteria that are outside this construct.

Do not artificially inflate the score. Do not artificially suppress it. Apply only deductions that directly correspond to demonstrated weaknesses in the nine defined capability dimensions, and state each deduction once.

END OF V3 COMMON CALIBRATION AND SCORING AUTHORITY

Evidence-based model assessments; not vendor certifications or endorsements. Historical documents retain their original wording and scope. Prepared for owner review; website publication is separate.

Historical v1.5 rubric and five-model synthesis · superseded for these two current tracks
100-Point Architecture Rubric

The historical v1.5 rubric and its five-model synthesis.

Evaluation Standard v1.5 governed the earlier assessment cycle documented in this historical table. It does not govern either September 28 successor score; the ten-dimension HF03/T01 method and the nine-dimension V3-derived method remain separate. Because the five runs had different access conditions, the final column reports the evidence-backed panel signal rather than manufacturing a synthetic per-dimension average.

v1.5 DimensionWeightWhat Evaluators Must EstablishFive-Model SignalEvidence / Limitation
AI / System Architecture15%Boundaries, modularity, interfaces, quality attributes, decisions and tradeoffs.ADVANCEDConsistent panel strength; source/runtime correspondence should be made explicit.
Problem Decomposition & Operational Translation15%Translate ambiguous operating problems into bounded components, scope, economics and acceptance criteria.ADVANCED / STRONGESTRepeated strength; Claude identifies MyFlightWatcher as the strongest single artifact.
Multi-Model & AI Orchestration10%Role separation, routing, specialization, review independence, fallback and cost logic.ADVANCED DESIGNLongitudinal routing/review telemetry remains thinner than the design.
Product / System Integration15%Coherent working integration across UI, workflow, backend, services, data and operations.ACCESS-LIMITEDLargest evidence sensitivity. PARTIAL evaluators could not inspect source/runtime artifacts.
API / Data / Tool Integration10%Provider abstraction, contracts, persistence, failure handling, quota and security boundaries.ADVANCED DESIGN / MODERATE PROOFStrong provider/data reasoning; implementation proof uneven across runs.
Workflow & State Architecture10%Explicit durable state, continuity, lifecycle, context preservation, delivery and recovery.ADVANCEDStrong persistence-first thinking; more real resume/fault/recovery traces needed.
Governance & Change Control10%Authority, versioning, bounded change, traceability, self-certification controls and anti-drift.ADVANCED DESIGNClaude found a live evaluator-package authority contradiction that must be closed.
QA / Debugging / Recovery10%Executed tests, defect detection, root cause, regression, rollback, security validation and recovery.PROFICIENT–ADVANCED DESIGN / LIMITED EXECUTION EVIDENCEMost important proof gap: raw tests, defects, incidents, CI, security scans and rollback evidence.
Scope Expansion via AI5%Breadth achieved through governed AI delegation while maintaining scope and quality control.ADVANCEDBroad portfolio supports capability; production maturity must remain case-specific.

Interpretation: architecture capability is Advanced at the design layer. The principal remediation opportunity is to convert specified controls into directly inspectable execution evidence and to standardize evaluator access.

Human-Inspired & Human-Designed AI Architecture

The differentiator is not who typed the code. It is who designed the system of decisions.

Peter's model treats AI as an engineering workforce while retaining human control of framing, constraints, architecture, tradeoffs, acceptance, remediation and final disposition. That is the capability the V3 construct was designed to measure.

Problem framing

Human-directed. Business ambiguity becomes bounded objectives, constraints, acceptance evidence and explicit non-goals.

Architecture & tradeoffs

Human-designed. Source authority, stack choices, integration boundaries and recovery design are deliberate decisions.

AI workforce direction

Orchestrated. Models and coding agents fill bounded roles across implementation, QA, review and remediation.

Acceptance & rejection

Controlled. Work is accepted against evidence; weak recommendations are rejected, repaired or rerouted.

Evidence & recovery

Traceable. Source identities, deterministic tests, negative-path evidence and banked results make architecture reviewable.

Scope multiplication

AI-enabled. One architect expands engineering reach across products, stacks and review surfaces without surrendering control.

The accepted 99.50 informed successor recognizes applied-AI architectural capability: translating complex objectives into bounded work, retaining human decision authority and supporting acceptance with inspectable evidence. The historical 97.00 V3 report remains a baseline, not the current headline.
Authority & Governance Crosswalk

Why the assessment used standards designed to find weaknesses.

NIST, ISO/IEC, IEEE, SEI, OWASP, MITRE, CSA, OpenSSF and SLSA are used because they carry standing with architects, engineering organizations, cloud/security teams, government programs and enterprises. They are demanding benchmarks: they expect traceability, controls, negative-path thinking, architectural rationale and evidence.

Selection rule: an authority belongs in the rubric only when it materially improves construct validity. Logos, prestige and citation count are insufficient. Every framework must answer a specific question, have an identifiable issuing body or open governance process, and be independently reviewable through a primary source.
NIST
U.S. National Institute of Standards and Technology

AI RMF 1.0 + Generative AI Profile

NIST is a U.S. federal standards and measurement institution whose stated core competencies include measurement science, rigorous traceability, and development/use of standards. AI RMF 1.0 was released in January 2023 after a consensus-driven public process; the GenAI Profile followed in July 2024.

Standing: government-developed, voluntary, cross-sector AI risk-management framework.
Used for: trustworthiness, governance, risk identification, measurement, monitoring, transparency, safety, privacy and lifecycle controls.
Independent review ↗ NIST AI RMF
ALIGNMENT: PARTIAL / DESIGN
ISO
ISO / IEC

ISO/IEC 42001:2023

ISO describes 42001 as the world's first AI management-system standard. ISO standards are developed through international technical experts, multi-stakeholder participation and consensus voting; ISO says a standard typically takes about three years from proposal to publication.

Standing: international consensus standard for establishing and continually improving an AI Management System.
Used for: governance, accountability, documented controls, continual improvement, risk/opportunity management and management-system discipline.
Independent review ↗ ISO 42001
ALIGNMENT: PARTIAL / DESIGN
42010
ISO / IEC / IEEE

ISO/IEC/IEEE 42010:2022

An international standard specifying requirements for architecture descriptions across software, systems, enterprises, systems-of-systems, product lines and related entities.

Standing: formal architecture-description standard; the edition used in this historical crosswalk was published in 2022.
Used for: stakeholders, concerns, viewpoints, views, architecture-description frameworks and traceable architectural communication.
Independent review ↗ ISO/IEC/IEEE 42010
ALIGNMENT: PARTIAL / DESIGN
SEI
Carnegie Mellon Software Engineering Institute

ATAM® + Quality Attribute Workshop

SEI introduced ATAM in the late 1990s and has refined it for decades. SEI's 2026 material describes ATAM as the leading method in software-architecture evaluation. QAW complements ATAM by identifying critical quality attributes before architecture is fully developed.

Standing: long-running architecture evaluation method from CMU SEI, with formal reports dating to 1998–2000.
Used for: business drivers, quality attributes, architectural risks, sensitivity points, tradeoffs, scenarios and mitigations.
Independent review ↗ SEI ATAM
APPLICATION: PARTIAL
OWASP
OWASP Foundation / GenAI Security Project

GenAI Top 10 · ASVS · SAMM

OWASP is a nonprofit, open global software-security community founded in 2001. Its LLM/GenAI security initiative began in 2023 and now publishes current GenAI risk guidance with a large international contributor community.

Standing: open, community-led application-security authority with widely used standards, tools and guidance.
Used for: prompt injection, excessive agency, insecure output handling, supply-chain risk, application-security verification and secure-development maturity.
Independent review ↗ OWASP GenAI
ALIGNMENT: LIMITED EXECUTION
MITRE
MITRE

ATLAS

MITRE is a not-for-profit operator of federally funded research and development centers and describes its role as providing objective, public-interest technical expertise. ATLAS provides a knowledge base of adversarial tactics and techniques for AI-enabled systems.

Standing: research-driven threat-modeling source backed by an institution with decades of government systems and cybersecurity work.
Used for: adversarial AI threat modeling, attack paths, agentic risks and mitigations.
Independent review ↗ MITRE ATLAS
ALIGNMENT: LIMITED / NOT EXERCISED
CSA
Cloud Security Alliance

AI Controls Matrix v1.1

CSA's 2026 AICM v1.1 is a vendor-neutral control framework for cloud-based AI systems with 247 control objectives across 18 domains and mappings to ISO 42001, ISO 27001 and other governance frameworks.

Standing: industry-led cloud/AI assurance framework designed for cross-framework control mapping.
Used for: structured AI controls, cloud responsibility, control ownership, model lifecycle, architecture relevance and governance crosswalks.
Independent review ↗ CSA AICM
ALIGNMENT: LIMITED / NOT EXERCISED
OSSF
OpenSSF / Linux Foundation ecosystem

OpenSSF Scorecard

An automated open-source project that checks repository practices such as branch protection, code review, dependency update tools, dangerous workflows, pinned dependencies, token permissions, vulnerabilities and security policy.

Standing: objective, reproducible open-source repository-health evidence; individual checks matter more than one aggregate score.
Used for: machine-verifiable repository governance and software-supply-chain evidence.
Independent review ↗ OpenSSF Scorecard repo
RESULT: NOT RUN IN THIS CYCLE
SLSA
SLSA / OpenSSF ecosystem

Supply-chain Levels for Software Artifacts

SLSA provides a framework for software build integrity and provenance. It is used here narrowly: to determine whether released evidence can be traced to controlled source and build processes.

Standing: recognized software-supply-chain provenance framework; not an architecture score.
Used for: provenance, artifact identity, build integrity, attestation and release discipline.
Independent review ↗ SLSA specification
ALIGNMENT: PARTIAL / DESIGN

Authority selection and evidentiary limits

Authority typeWhy includedWhat it can legitimately supportWhat it cannot prove
Government / standards institutionsFormal, traceable, multi-stakeholder or public processes.Alignment with recognized risk, governance, measurement and architecture practices.That Peter or VPL is certified unless a real audit/certification occurs.
Architecture evaluation methodsThey test tradeoffs and quality attributes rather than code aesthetics.Quality of architecture reasoning, risks, sensitivities and tradeoffs.Production correctness without implementation evidence.
Open security communitiesRapid, transparent, expert-driven response to current threats.Coverage of known application and AI attack classes.Absence of unknown vulnerabilities.
Machine-verifiable open toolsReproducible checks reduce LLM subjectivity.Specific repository/security/provenance observations.Overall architectural sophistication.
Evidence Portfolio

Representative systems, not a volume contest.

The portfolio is selected for construct coverage: architecture, breadth, evolution, integration, state, governance, recovery, production maturity, AI leverage and meta-architecture.

MySmartRouter

Multi-model routing, provider abstraction, state, API integration, QA, recovery and version progression.

VPL Build Framework V1→V5

Evolution from procedural controls and V3 source/agent governance to the V4 REV006 foundation and reviewed V5 preservation, recovery and evidence controls. Runtime persistence remains a separate acceptance gate.

MyRocketStudio

Product/system decomposition, program architecture, decision locks and implementation-vs-design distinction.

ResumeRocket

Multi-component workflow, scoring/analysis, generation, persistence, versioning and QA governance.

MyFlightWatcher

External providers, scheduling, database, alerts, analytics, hosting constraints, incidents and recovery.

RocketCapture

Extension architecture, structured capture, metadata preservation, evidence packaging and QA locks.

MyIdeaMax V2

Operational use of project/state/decisions/backlog/escalations/directives under V3 governance.

VoiceVibe V2

Tests whether architectural discipline persists in a lower-complexity standalone implementation.

Secondary candidates for reviewer challenge: RocketBuilder, MyTurboGPT, RocketAIStudio and RocketCore/myrocketsuite Waves. They enter the primary set only if they add unique construct coverage.

View Assessed Source Code · six exact-byte source artifacts

Assessed source selection and reproducible identity

This is the exact-byte selection already prepared for public inspection from the frozen final grading packet: five Python source files and one patch/diff. It is not the full framework tree, a runnable product release, or the private MySmartRouter source package.

Frozen candidate Git tree: ddc0e4fd644b59a29d1bb7a7f647cb89f2556f4d
Final grading input SHA-256: 7bd8eacbc506414292e80849521317184b219eb1667530f6ef861c52dbca6512

action1/apply_owner_closeout_R03_UNCHANGED.py

15,710 bytes · SHA-256 81a0620364bae8771314b4e8a21010735c9db39a86298f363934b3194305420e

Download exact source file

#!/usr/bin/env python3
"""Apply only the approved R03 documentary addendum; never alter frozen source.

Exit 0 means deterministic composition/validation succeeded, NOT semantic approval.
The unchanged R01 verifier is used for baseline source checks only, without any
adjudication. No candidate, Action 3/4, or old unit-test suite is executed.
"""
from __future__ import annotations
import argparse, copy, hashlib, importlib.util, json, sys, zipfile
from pathlib import Path

PRIMARY_SHA = 'd0054ef48173343dc3ba7d08b1a3bb031019b3f0b459c44f8546ae86a27d2c8b'
BASE_RECEIPT_SHA = '3893f7cae4a710cb2fd20de405e769b419e5d46b9775c6c55022cbab3a03c3fb'
GL = 'controls/VPL_V5_06_GUIDELINE_REGISTRY_R02.json'
AGENT = 'controls/VPL_V5_06_AGENT_REGISTRY_R02.json'
O1 = 'O1_ADOPTED_AMENDMENTS.md'
V4_GL = 'Treat model persistence as untrusted. Load every canonical V4 operating control with equal persistence priority; evaluate trigger/risk/applicability only after complete load. Prove semantic completeness with manifest/receipt, balanced challenge coverage and immediate behavioral test. Self-report, recap or selective retrieval is not evidence. Consequential mutation requires PERSISTENCE_STATE=CURRENT.'
APPROVAL = 'Approve replacing the A01/M26 mandatory-when-proven 15-minute Hostinger producer with invocation-bound V5 persistence. Cron is optional and cannot establish CURRENT. Retain every V4 GL-15 safeguard; V5 GL-15 is additive. This is design approval only, not deployment or score approval.'
CLAUSES = ['Treat model persistence as untrusted.', 'immediate behavioral test', 'selective retrieval is not evidence']

class Rejected(ValueError): pass

def require(value, message):
    if not value: raise Rejected(message)

def sha(b): return hashlib.sha256(b).hexdigest()
def canonical(x): return (json.dumps(x, sort_keys=True, ensure_ascii=False, allow_nan=False, separators=(',', ':'))+'\n').encode()
def pretty(x): return (json.dumps(x, indent=2, ensure_ascii=False, allow_nan=False)+'\n').encode()

def load_json(raw):
    def pairs(items):
        d={}
        for k,v in items:
            require(k not in d, 'duplicate JSON key: '+k); d[k]=v
        return d
    def bad(v): raise Rejected('nonfinite JSON number: '+v)
    return json.loads(raw, object_pairs_hook=pairs, parse_constant=bad)

def row(obj, array, id):
    a=[r for r in obj[array] if r.get('id')==id]
    require(len(a)==1, 'missing/duplicate row: '+id)
    return a[0]

def load_baseline(root: Path):
    """Verify reviewed archive and every expanded inner member before import."""
    raw=(root/'originals/VPL_ACTION1_SEMANTIC_CLAUDE_READY_R01.zip').read_bytes()
    require(sha(raw)==PRIMARY_SHA,'wrong reviewed primary archive')
    base=root/'prior_reviewed_R01'
    prefix='ACTION1_REVIEW_DELIVERY_R01/ACTION1_SEMANTIC_R01/'
    with zipfile.ZipFile(root/'originals/VPL_ACTION1_SEMANTIC_CLAUDE_READY_R01.zip') as z:
        names=[n for n in z.namelist() if n.startswith(prefix) and not n.endswith('/')]
        expected={n[len(prefix):] for n in names}
        actual={p.relative_to(base).as_posix() for p in base.rglob('*') if p.is_file()}
        require(expected==actual and len(expected)==35,'expanded reviewed input file set differs')
        for n in names:
            p=base/n[len(prefix):]
            require(not p.is_symlink() and p.read_bytes()==z.read(n),'expanded prior bytes differ: '+n)
    spec=importlib.util.spec_from_file_location('accepted_action1_r01',base/'source/verify_semantic_equivalence.py')
    module=importlib.util.module_from_spec(spec); spec.loader.exec_module(module)
    authority={n:(base/'input/v4_authority'/n).read_bytes() for n in module.AUTH_PINS}
    candidate=(base/'input/VPL_V5_R08_HF03_T01_R01.zip').read_bytes()
    rules=(base/'rules/INVARIANT_MAPPINGS_R01.json').read_bytes()
    receipt=module.evaluate(candidate,authority,rules)
    require(sha(module.canonical(receipt))==BASE_RECEIPT_SHA,'baseline mechanical receipt differs')
    require(receipt['resolved']==0 and receipt['unresolved']==328,'baseline was adjudicated')
    return module, module.read_archive(candidate), authority, load_json(rules), receipt

def read_new_inputs(root: Path):
    pins=load_json((root/'INPUT_BINDINGS_R03.json').read_bytes())
    require(set(pins)=={'OWNER_PERSISTENCE_AMENDMENT_R03.md','OWNER_DECISION_RECEIPT_R03.json','originals/EXACT_CLOSEOUT_CORRECTIONS_R02.json','originals/OWNER_PERSISTENCE_DECISION_DRAFT_R02.md','originals/CLAUDE_ACTION1_CONFIRMATION_REPORT_R01.md','originals/CLAUDE_ACTION1_CONFIRMATION_R01.zip','originals/VPL_ACTION1_SEMANTIC_CLAUDE_READY_R01.zip'},'unexpected input binding set')
    for name,pin in pins.items():
        p=root/name
        require(not p.is_symlink() and sha(p.read_bytes())==pin,'new input hash mismatch: '+name)
    amendment=(root/'OWNER_PERSISTENCE_AMENDMENT_R03.md').read_bytes()
    owner=load_json((root/'OWNER_DECISION_RECEIPT_R03.json').read_bytes())
    require(owner.get('status')=='ADOPTED_DESIGN_ONLY','owner decision not adopted')
    require(owner.get('owner_reply_verbatim')=='approve' and owner.get('approved_proposition_verbatim')==APPROVAL,'owner proposition differs')
    require(owner.get('amendment_sha256')==sha(amendment),'owner amendment binding differs')
    require(owner.get('draft_sha256')==sha((root/'originals/OWNER_PERSISTENCE_DECISION_DRAFT_R02.md').read_bytes()),'wrong approved draft')
    for k in ['semantic_model_approved','r2_m01_closed','runtime_current_certified','scores_changed','deployment_authorized']:
        require(owner.get(k) is False,'approval scope widened: '+k)
    patches=load_json((root/'originals/EXACT_CLOSEOUT_CORRECTIONS_R02.json').read_bytes())
    require(patches.get('candidate_git_tree')=='ddc0e4fd644b59a29d1bb7a7f647cb89f2556f4d','wrong patch baseline')
    return amendment,owner,patches

def apply_context(data, entries, amendment, patches):
    require(len(entries)==35 and len({e['path'] for e in entries})==35,'source coverage incomplete')
    effective={e['path']:data[e['path']] for e in entries}
    ps=patches['patches']
    require(len(ps)==2,'unexpected patch count')
    require([(p['file'],p['array'],p['row_id'],p['field']) for p in ps]==[
        (GL,'rules','VPL-GL-15','requirement'),
        (AGENT,'agents','VPL-A01','historical_rev006_design.contract_authority')], 'unexpected patch scope')
    for p in ps:
        obj=load_json(effective[p['file']]); target=row(obj,p['array'],p['row_id'])
        require(canonical(target)==canonical(p['expected_row']),'patch precondition differs: '+p['row_id'])
        if p['row_id']=='VPL-GL-15':
            expected=('The V4 REV006 VPL-GL-15 requirement is retained in full and remains mandatory. The following V5 contract is additive, not a replacement; none of its wording waives an inherited V4 safeguard.\n\n'+V4_GL+'\n\nAdditional V5 contract:\n'+target['requirement'])
            require(p['replacement']==expected,'GL15 staged replacement lost complete inheritance')
            target['requirement']=expected
        else:
            require(target['historical_rev006_design']['contract_authority']==p['old'],'old A01 citation differs')
            require(p['replacement']=='build_framework_V4.0 Final_Rev6.html#persistence','incorrect A01 correction')
            target['historical_rev006_design']['contract_authority']=p['replacement']
        effective[p['file']]=pretty(obj)
    effective[O1]=data[O1]+'\n\n---\n\n## Later owner amendment — September 28, 2026 / R03\n\n'.encode()+amendment
    return effective

def assemble_bundle(effective, entries):
    header=("# VPL V5 — effective closeout context R03 (source-bound addendum)\n\n"
            "Base: frozen HF03/T01, not overwritten or promoted. Published general authority remains V4 FINAL / REV006.\n\n"
            "For this versioned V5 design closeout only, the later owner decision appended to Source 4 (O1) replaces the A01/M26 mandatory-when-proven 15-minute producer with invocation-bound proof and optional cron. It preserves every V4 GL-15 safeguard; V5 GL-15 is additive. Only the named design choice is newly adopted; no blanket approval of all candidate overlays is inferred. The same named producer choice applies to its repeated statements; all other inherited triggers and safeguards remain intact.\n\n"
            "All 35 base sources are included in original order. Source 4 has the appended actual owner decision; Source 26 has the Rev6 provenance correction; Source 27 has full V4 GL-15 retention. All other source bytes are unchanged. The complete original context and its historical claims remain available in the frozen archive. This is not an installable/resealed 451-file candidate.\n\n"
            "On an unresolved conflict: STOP for source/owner resolution. No runtime CURRENT, semantic model approval, R2-M01 closure or score is certified. The remaining independent review must address this complete context and the three existing registry wrapper differences.\n").encode()
    out=header
    for i,e in enumerate(entries,1):
        path=e['path']; raw=effective[path]
        out+=(f'\n\n## Source {i}: {path}\n\nSHA-256: {sha(raw)}\n\n<!-- BEGIN PINNED SOURCE -->\n').encode()+raw+b'\n<!-- END PINNED SOURCE -->\n'
    return out

def compose(root: Path):
    module,data,authority,old_rules,base=load_baseline(root)
    amendment,owner,patches=read_new_inputs(root)
    entries=base['v5_source_inventory']
    effective=apply_context(data,entries,amendment,patches)
    blocks,_=module.source_blocks(authority[module.V4_NAME],load_json(authority['VPL_V4_EQUAL_LOAD_MANIFEST_REV006.json']))
    require(row(load_json(blocks['V4_LOCKED_GUIDELINES.json']),'rules','VPL-GL-15')['requirement']==V4_GL,'V4 complete GL15 differs')
    inv=[{'path':e['path'],'base_sha256':e['sha256'],'sha256':sha(effective[e['path']]),'size_bytes':len(effective[e['path']]),'changed':effective[e['path']]!=data[e['path']]} for e in entries]
    require({r['path'] for r in inv if r['changed']}=={O1,GL,AGENT},'unexpected effective source change')
    bundle=assemble_bundle(effective,entries)
    v4=authority[module.V4_NAME]; require(bundle.count(v4)==1,'V4 not uniquely inherited')
    offset=bundle[:bundle.index(v4)].count(b'\n')
    maps=copy.deepcopy(old_rules['span_mappings'])
    # Inspect actual mapping shape rather than synthesizing a new 328-row inventory.
    for item in maps:
        dest=item['destination_span']; src=item['source_span']
        dest['file']='EFFECTIVE_CONTEXT_BUNDLE_R03.md'
        dest['start_line']=src['start_line']+offset; dest['end_line']=src['end_line']+offset
        left=b''.join(v4.splitlines(keepends=True)[src['start_line']-1:src['end_line']])
        right=b''.join(bundle.splitlines(keepends=True)[dest['start_line']-1:dest['end_line']])
        require(left==right and sha(left)==src['span_sha256'],'effective destination span differs')
    revised=copy.deepcopy(old_rules)
    revised['kind']='ACTION1_SOURCE_BOUND_RULE_MODEL_WITH_OWNER_ADDENDUM_R03'
    revised['approval_status']='OWNER_DESIGN_ADOPTED_SEMANTIC_ADEQUACY_PENDING'
    revised['base_rule_sha256']=sha((root/'prior_reviewed_R01/rules/INVARIANT_MAPPINGS_R01.json').read_bytes())
    revised['span_mappings']=maps
    revised['v5_source_inventory']=[{k:v for k,v in e.items() if k in ('path','sha256','size_bytes')} for e in inv]
    revised['base_registry_comparison']=revised.pop('registry_comparison')
    revised['base_bounded_translation_checks']=revised.pop('bounded_translation_checks')
    combined={**data,**effective}
    revised['registry_comparison']=module.registry_comparison(blocks,combined)
    revised['effective_context_bundle_sha256']=sha(bundle)
    revised['owner_design_binding']={'record_sha256':sha((root/'OWNER_DECISION_RECEIPT_R03.json').read_bytes()),'amendment_sha256':sha(amendment),'owner_reply':'approve','scope':'A01/M26_DESIGN_AND_FULL_GL15_ADDITIVE_RETENTION_ONLY','independent_semantic_approval':False}
    revised['rule_id']='SOURCE_BOUND_VERBATIM_INHERITANCE_WITH_EXPLICIT_OVERLAY_REVIEW_R03'
    revised['rule_definition']['r03_precedence']='Apply only the explicitly adopted A01/M26 design change, retain GL15 completely and add V5 wording. Other inherited triggers remain; aggregate overlay adequacy still requires independent review.'
    gl=row(load_json(effective[GL]),'rules','VPL-GL-15')['requirement']
    revised['added_gl15_literal_checks']=[{'literal':c,'present_in_effective_GL15':c in gl,'proof':'literal retention, not behavioral entailment'} for c in CLAUSES]
    revised['complete_v4_gl15_retained']=V4_GL in gl
    revised['complete_old_v5_gl15_retained']=row(load_json(data[GL]),'rules','VPL-GL-15')['requirement'] in gl
    revised['o1_prior_bytes_preserved']=effective[O1].startswith(data[O1])
    rules_raw=pretty(revised)
    receipt={'kind':'ACTION1_OWNER_CLOSEOUT_MECHANICAL_RECEIPT_R03','result':'CORRECTIONS_APPLIED_SEMANTIC_ADEQUACY_PENDING','owner_design_decision':'ADOPTED','base_mechanical_receipt_sha256':sha(module.canonical(base)),'candidate_archive_sha256':module.CANDIDATE_SHA,'candidate_git_tree':module.CANDIDATE_TREE,'candidate_files':451,'frozen_candidate_unchanged':True,'effective_sources':35,'unchanged_effective_sources':32,'changed_effective_sources':[e for e in inv if e['changed']],'mechanically_verified_spans':328,'semantic_resolved':0,'semantic_unresolved':328,'r2_m01_closed':False,'independent_semantic_approval_supplied':False,'full_v4_gl15_retained':revised['complete_v4_gl15_retained'],'full_old_v5_gl15_retained':revised['complete_old_v5_gl15_retained'],'missing_gl15_literals_now_present':all(c in gl for c in CLAUSES),'a01_reference_corrected':row(load_json(effective[AGENT]),'agents','VPL-A01')['historical_rev006_design']['contract_authority'],'owner_amendment_sha256':sha(amendment),'effective_context_bundle_sha256':sha(bundle),'revised_rules_sha256':sha(rules_raw),'frozen_candidate_source_code_changed':False,'candidate_suite_executed':False,'old_verifier_test_suite_rerun':False,'actions3_4_rerun':False,'scores_changed':False,'deployments_performed':False,'execution_boundary':'New composition and literal-preservation checks plus accepted baseline source verification only; no semantic adjudication.'}
    outputs={'effective_context/'+n:b for n,b in effective.items()}
    outputs.update({'EFFECTIVE_CONTEXT_BUNDLE_R03.md':bundle,'SEMANTIC_RULES_R03.json':rules_raw,'EFFECTIVE_CONTEXT_INVENTORY_R03.json':pretty(inv),'OWNER_CLOSEOUT_MECHANICAL_RECEIPT_R03.json':pretty(receipt)})
    return outputs, receipt

def write_new(out:Path, outputs:dict):
    require(not out.exists(),'output directory exists; refusing overwrite')
    require(out.parent.is_dir(),'parent output directory missing')
    out.mkdir()
    for n,b in outputs.items():
        p=out/n;p.parent.mkdir(parents=True,exist_ok=True)
        with p.open('xb') as f:f.write(b)

def main():
    ap=argparse.ArgumentParser(description=__doc__);ap.add_argument('--root',type=Path,default=Path(__file__).resolve().parents[1]);ap.add_argument('--output',type=Path,required=True)
    a=ap.parse_args()
    try:
        require(not a.output.exists(),'output directory exists; refusing overwrite')
        out,r=compose(a.root.resolve());write_new(a.output,out)
        print(json.dumps({'result':r['result'],'sources':35,'changed_sources':3,'spans':328,'semantic_resolved':0,'semantic_unresolved':328,'files_written':len(out),'receipt_sha256':sha(out['OWNER_CLOSEOUT_MECHANICAL_RECEIPT_R03.json'])},sort_keys=True));return 0
    except (Rejected,ValueError,OSError,KeyError,TypeError,zipfile.BadZipFile) as e:
        print(json.dumps({'result':'REJECTED','error':str(e)},sort_keys=True),file=sys.stderr);return 2
if __name__=='__main__':sys.exit(main())
action1/verify_owner_closeout_R03_UNCHANGED.py

7,827 bytes · SHA-256 b60c542bc0927cec689d347f0f3fa6042953af85dfca4bebeb0970d2d3dbfe8f

Download exact source file

#!/usr/bin/env python3
"""Read-only verification of generated R03 files; does not import the composer."""
from __future__ import annotations
import argparse,copy,hashlib,io,json,sys,zipfile
from pathlib import Path

CANDIDATE='c3bd53d775752494d982183cc71b551af6b4bf2c879416d6866a859e64fd28ac'
V4='d4e75170b791b4458d60ea39f35fa165a81b89fce236c12f15f40af038a95d62'
GL='controls/VPL_V5_06_GUIDELINE_REGISTRY_R02.json'
AG='controls/VPL_V5_06_AGENT_REGISTRY_R02.json'
O1='O1_ADOPTED_AMENDMENTS.md'
def sha(b):return hashlib.sha256(b).hexdigest()
def must(v,s):
    if not v:raise ValueError(s)
def j(b):return json.loads(b)
def getrow(d,a,id):
    rows=[x for x in d[a] if x['id']==id];must(len(rows)==1,'row missing/duplicate');return rows[0]

def verify(root:Path,generated:Path):
    b=root/'prior_reviewed_R01';raw=(b/'input/VPL_V5_R08_HF03_T01_R01.zip').read_bytes()
    must(sha(raw)==CANDIDATE,'wrong frozen candidate')
    with zipfile.ZipFile(io.BytesIO(raw)) as z:
        d={i.filename.split('/',1)[1]:z.read(i) for i in z.infolist() if not i.is_dir()}
    must(len(d)==451,'candidate count differs')
    entries=j(d['SEMANTIC_INPUTS_R08.json'])['files'];must(len(entries)==35,'expected 35 source contexts')
    inv=j((generated/'EFFECTIVE_CONTEXT_INVENTORY_R03.json').read_bytes())
    must([x['path'] for x in inv]==[x['path'] for x in entries],'context source order differs')
    must({p.relative_to(generated/'effective_context').as_posix() for p in (generated/'effective_context').rglob('*') if p.is_file()}=={e['path'] for e in entries},'missing/extra effective file')
    owner=(root/'OWNER_PERSISTENCE_AMENDMENT_R03.md').read_bytes()
    decision=j((root/'OWNER_DECISION_RECEIPT_R03.json').read_bytes())
    must(decision['owner_reply_verbatim']=='approve' and decision['status']=='ADOPTED_DESIGN_ONLY','no owner design decision')
    must(decision['amendment_sha256']==sha(owner),'decision binding failed')
    v4=(b/'input/v4_authority/VPL_V4_SEMANTIC_LOAD_BUNDLE_REV006.md').read_bytes();must(sha(v4)==V4,'wrong V4 source')
    manifest=j((b/'input/v4_authority/VPL_V4_EQUAL_LOAD_MANIFEST_REV006.json').read_bytes())
    slot=next(x for x in manifest['canonical_sources'] if x['name']=='V4_LOCKED_GUIDELINES.json')
    start=v4.index(b'# BEGIN CANONICAL SOURCE: V4_LOCKED_GUIDELINES.json\n\n')+len(b'# BEGIN CANONICAL SOURCE: V4_LOCKED_GUIDELINES.json\n\n')
    guidelines=v4[start:start+slot['bytes']];must(sha(guidelines)==slot['sha256'],'V4 guideline block mismatch')
    v4req=getrow(j(guidelines),'rules','VPL-GL-15')['requirement']
    oldreq=getrow(j(d[GL]),'rules','VPL-GL-15')['requirement']
    fullreq=('The V4 REV006 VPL-GL-15 requirement is retained in full and remains mandatory. The following V5 contract is additive, not a replacement; none of its wording waives an inherited V4 safeguard.\n\n'+v4req+'\n\nAdditional V5 contract:\n'+oldreq)
    eff={};changed=[]
    for old,new in zip(entries,inv):
        n=old['path'];v=(generated/'effective_context'/n).read_bytes();eff[n]=v
        must(new['sha256']==sha(v) and new['size_bytes']==len(v) and new['base_sha256']==sha(d[n]),'inventory binding failed: '+n)
        actual_change=v!=d[n];must(new['changed']==actual_change,'false change classification')
        if actual_change:changed.append(n)
        if n==GL:
            expected=j(d[n]);getrow(expected,'rules','VPL-GL-15')['requirement']=fullreq
            must(j(v)==expected,'GL15 changed beyond exact additive correction')
        elif n==AG:
            expected=j(d[n]);getrow(expected,'agents','VPL-A01')['historical_rev006_design']['contract_authority']='build_framework_V4.0 Final_Rev6.html#persistence'
            must(j(v)==expected,'A01 changed beyond exact citation correction')
        elif n==O1:
            must(v==d[n]+'\n\n---\n\n## Later owner amendment — September 28, 2026 / R03\n\n'.encode()+owner,'O1 original not preserved with exact appended decision')
        else:must(v==d[n],'unrelated source changed: '+n)
    must(set(changed)=={GL,AG,O1},'unexpected changed set')
    bundle=(generated/'EFFECTIVE_CONTEXT_BUNDLE_R03.md').read_bytes()
    header=bundle.split(b'\n\n## Source 1: ',1)[0]
    must(b'Only the named design choice is newly adopted' in header and b'On an unresolved conflict: STOP' in header,'precedence limitation missing')
    reconstructed=header
    for i,e in enumerate(entries,1):
        n=e['path'];v=eff[n]
        reconstructed+=(f'\n\n## Source {i}: {n}\n\nSHA-256: {sha(v)}\n\n<!-- BEGIN PINNED SOURCE -->\n').encode()+v+b'\n<!-- END PINNED SOURCE -->\n'
    must(reconstructed==bundle,'effective context cannot be reconstructed')
    rules_raw=(generated/'SEMANTIC_RULES_R03.json').read_bytes();rules=j(rules_raw)
    base_rules=j((b/'rules/INVARIANT_MAPPINGS_R01.json').read_bytes())
    must(rules['approval_status']=='OWNER_DESIGN_ADOPTED_SEMANTIC_ADEQUACY_PENDING','false semantic approval')
    must(len(rules['span_mappings'])==328,'span inventory incomplete')
    vl=v4.splitlines(keepends=True);bl=bundle.splitlines(keepends=True)
    must(bundle.count(v4)==1,'V4 not unique in effective bundle')
    offset=bundle[:bundle.index(v4)].count(b'\n')
    for before,after in zip(base_rules['span_mappings'],rules['span_mappings']):
        old=copy.deepcopy(before);new=copy.deepcopy(after);a=old.pop('destination_span');s=new.pop('destination_span')
        must(old==new,'non-destination mapping data changed')
        src=after['source_span']
        must(s=={**a,'file':'EFFECTIVE_CONTEXT_BUNDLE_R03.md','start_line':src['start_line']+offset,'end_line':src['end_line']+offset},'bad updated range')
        x=b''.join(vl[src['start_line']-1:src['end_line']]);y=b''.join(bl[s['start_line']-1:s['end_line']])
        must(x==y and sha(x)==src['span_sha256'],'span not preserved')
    r=j((generated/'OWNER_CLOSEOUT_MECHANICAL_RECEIPT_R03.json').read_bytes())
    must(r['semantic_resolved']==0 and r['semantic_unresolved']==328 and r['r2_m01_closed'] is False,'unearned closure')
    must(r['effective_context_bundle_sha256']==sha(bundle) and r['revised_rules_sha256']==sha(rules_raw),'receipt source binding mismatch')
    must(rules['effective_context_bundle_sha256']==sha(bundle),'rules context binding mismatch')
    must(rules['owner_design_binding']['record_sha256']==sha((root/'OWNER_DECISION_RECEIPT_R03.json').read_bytes()),'rules owner binding mismatch')
    for literal in ['Treat model persistence as untrusted.','immediate behavioral test','selective retrieval is not evidence']:
        must(literal in fullreq,'retained invariant missing')
    return {'kind':'OWNER_CLOSEOUT_SAVED_DATA_CHECK_R03','result':'PASS_SAVED_DATA_ONLY','original_candidate_files':451,'complete_context_sources':35,'unchanged_sources':32,'changed_sources':changed,'source_spans':328,'full_V4_GL15_and_full_old_V5_GL15_preserved':True,'three_missing_clauses_retained':True,'owner_design_adopted':True,'semantic_approval':False,'semantic_resolved':0,'semantic_unresolved':328,'r2_m01_closed':False,'context_bundle_sha256':sha(bundle),'rules_sha256':sha(rules_raw),'composer_imported':False,'independent_review':False}

def main():
    p=argparse.ArgumentParser(description=__doc__);p.add_argument('--root',type=Path,default=Path(__file__).resolve().parents[1]);p.add_argument('--generated',type=Path,required=True);p.add_argument('--output',type=Path)
    a=p.parse_args()
    try:
        if a.output:must(not a.output.exists(),'refusing overwrite')
        result=verify(a.root,a.generated);raw=(json.dumps(result,sort_keys=True,indent=2)+'\n').encode()
        if a.output:
            with a.output.open('xb') as f:f.write(raw)
        print(raw.decode());return 0
    except (OSError,ValueError,KeyError,TypeError) as e:print(json.dumps({'result':'REJECTED','error':str(e)}));return 2
if __name__=='__main__':sys.exit(main())
action1/verify_semantic_equivalence_R01_UNCHANGED.py

31,585 bytes · SHA-256 442d271ee56dade4fe6c4f7e7a002363a0471b1b5bc20b583d9b16d927280547

Download exact source file

#!/usr/bin/env python3
"""Source-bound Action 1 verifier. No candidate imports, execution, or source writes.

A successful mechanical run does NOT assert semantic equivalence. Unless an
externally approved, hash-pinned model adjudication is supplied, exit 3 and keep
semantic statuses UNRESOLVED. Exit 2 is malformed/untrusted input. Exit 0 is only
bounded specification equivalence under that declared approval, never runtime.
"""
from __future__ import annotations
import argparse
import hashlib
import io
import json
import re
import stat
import sys
import zipfile
from pathlib import Path, PurePosixPath
from typing import Any

CANDIDATE_SHA = 'c3bd53d775752494d982183cc71b551af6b4bf2c879416d6866a859e64fd28ac'
CANDIDATE_TREE = 'ddc0e4fd644b59a29d1bb7a7f647cb89f2556f4d'
ROOT_NAME = 'VPL_V5_R08_HF03_T01_R01'
V4_NAME = 'VPL_V4_SEMANTIC_LOAD_BUNDLE_REV006.md'
V4_SHA = 'd4e75170b791b4458d60ea39f35fa165a81b89fce236c12f15f40af038a95d62'
V5_NAME = 'VPL_V5_SEMANTIC_LOAD_BUNDLE_R08.md'
MAP = 'controls/V4_PRESERVATION_MAP_HF02.json'
MATRIX = 'controls/MANUAL_LIGHT_PRESERVATION_MATRIX_R02.md'
AUTH_PINS = {
    V4_NAME: V4_SHA,
    'VPL_V4_EQUAL_LOAD_MANIFEST_REV006.json': '1e7ce7be27906b1f3251735a1f38c3c14b7d34e5ded7d4679938c5e68d242aea',
    'PERSISTENCE_CHALLENGE_MATRIX_REV006.json': 'f3e224d4c18a5aa6f2efe3708a9d62607660965d3db446078e7c02a3f10e7ae9',
    'REV006_SHA256SUMS.txt': 'ccf8bce20b37c6f6613abc4c855734332ea7f37edff6f3ced57e590b08e32c98',
}
REGISTRIES = (
    ('AGENT_REGISTRY.json', 'controls/VPL_V5_06_AGENT_REGISTRY_R02.json', 'agents'),
    ('V4_MODULE_REGISTRY_FINAL.json', 'controls/VPL_V5_06_MODULE_REGISTRY_R02.json', 'modules'),
    ('V4_LOCKED_GUIDELINES.json', 'controls/VPL_V5_06_GUIDELINE_REGISTRY_R02.json', 'rules'),
)
MODEL_RULE = 'SOURCE_BOUND_VERBATIM_INHERITANCE_WITH_EXPLICIT_OVERLAY_REVIEW_R01'

class InvalidInput(ValueError):
    pass

def require(condition: bool, message: str) -> None:
    if not condition:
        raise InvalidInput(message)

def sha(b: bytes) -> str:
    return hashlib.sha256(b).hexdigest()

def canonical(x: Any) -> bytes:
    return (json.dumps(x, sort_keys=True, ensure_ascii=False, allow_nan=False,
                       separators=(',', ':')) + '\n').encode('utf-8')

def load_json(b: bytes) -> Any:
    def pairs(items):
        out = {}
        for k, v in items:
            require(k not in out, 'duplicate JSON key: ' + k)
            out[k] = v
        return out
    def bad_constant(value):
        raise InvalidInput('non-finite JSON value: ' + value)
    try:
        return json.loads(b.decode('utf-8'), object_pairs_hook=pairs,
                          parse_constant=bad_constant)
    except (UnicodeError, json.JSONDecodeError) as e:
        raise InvalidInput('invalid UTF-8 JSON') from e

def same(a: Any, b: Any) -> bool:
    return canonical(a) == canonical(b)

def valid_path(name: str) -> str:
    p = PurePosixPath(name)
    require(bool(name) and not name.startswith('/') and '\\' not in name and
            ':' not in name and '\x00' not in name and all(x not in ('', '.', '..')
            for x in name.split('/')), 'unsafe path: ' + repr(name))
    require(p.as_posix() == name, 'noncanonical path')
    return name

def read_archive(raw: bytes) -> dict[str, bytes]:
    """In-memory reads only; reject duplicates, traversal and nonregular members."""
    require(len(raw) <= 25_000_000, 'archive exceeds bounded input size')
    try:
        with zipfile.ZipFile(io.BytesIO(raw)) as z:
            require(len(z.infolist()) <= 2000, 'too many archive members')
            seen, data, total = set(), {}, 0
            for item in z.infolist():
                name = item.filename.rstrip('/') if item.is_dir() else item.filename
                valid_path(name)
                require(item.filename not in seen, 'duplicate archive member')
                seen.add(item.filename)
                mode = item.external_attr >> 16
                require(not stat.S_ISLNK(mode), 'archive symlink forbidden')
                require(not mode or stat.S_IFMT(mode) in (0, stat.S_IFREG, stat.S_IFDIR),
                        'nonregular archive member')
                require(not (item.flag_bits & 1), 'encrypted archive forbidden')
                if item.is_dir():
                    continue
                require(name.startswith(ROOT_NAME + '/'), 'wrong candidate root')
                total += item.file_size
                require(total <= 75_000_000 and item.file_size <= 10_000_000,
                        'uncompressed size exceeds bound')
                rel = name[len(ROOT_NAME) + 1:]
                data[valid_path(rel)] = z.read(item)
            return data
    except (zipfile.BadZipFile, RuntimeError, EOFError) as e:
        raise InvalidInput('invalid candidate archive') from e

def git_tree(data: dict[str, bytes]) -> str:
    tree: dict[str, Any] = {}
    for name, b in data.items():
        parts = valid_path(name).split('/')
        here = tree
        for part in parts[:-1]:
            require(not isinstance(here.get(part), bytes), 'path/file collision')
            here = here.setdefault(part, {})
        require(parts[-1] not in here, 'duplicate tree leaf')
        here[parts[-1]] = b
    def obj(kind: str, b: bytes) -> bytes:
        return hashlib.sha1(kind.encode() + b' ' + str(len(b)).encode() + b'\0' + b).digest()
    def digest(node) -> bytes:
        out = b''
        for name in sorted(node, key=lambda n: (n + ('/' if isinstance(node[n], dict) else '')).encode()):
            v = node[name]
            out += (b'40000' if isinstance(v, dict) else b'100644') + b' ' + name.encode() + b'\0'
            out += digest(v) if isinstance(v, dict) else obj('blob', v)
        return obj('tree', out)
    return digest(tree).hex()

def source_blocks(v4: bytes, manifest: dict) -> tuple[dict[str, bytes], list[dict]]:
    """Recover exact original files from named byte-counted V4 transport blocks."""
    require(manifest.get('canonical_source_count') == 29, 'authority must bind 29 sources')
    require(manifest.get('semantic_bundle_sha256') == sha(v4), 'manifest/V4 hash mismatch')
    require(manifest.get('semantic_bundle_bytes') == len(v4), 'manifest/V4 length mismatch')
    declared = manifest.get('canonical_sources')
    require(isinstance(declared, list) and len(declared) == 29, 'incomplete canonical source list')
    blocks, observations = {}, []
    begins = re.findall(rb'^# BEGIN CANONICAL SOURCE: (.+)$', v4, re.M)
    require(len(begins) == 29 and len(set(begins)) == 29, 'missing/duplicate source blocks')
    require([x.decode() for x in begins] == [d['name'] for d in declared], 'source order differs')
    manifest_lines = re.findall(rb'^- `([^`]+)` \xe2\x80\x94 ([\d,]+) bytes \xe2\x80\x94 `([a-f0-9]{64})`$', v4, re.M)
    observed_manifest = [{'name': n.decode(), 'bytes': int(s.replace(b',', b'')),
                          'sha256': h.decode()} for n, s, h in manifest_lines]
    require(same(observed_manifest, declared), 'embedded and recovered manifest differ')
    for row in declared:
        name = valid_path(row['name'])
        require(name not in blocks and type(row['bytes']) is int and row['bytes'] > 0,
                'invalid canonical source row')
        marker = b'# BEGIN CANONICAL SOURCE: ' + name.encode() + b'\n\n'
        require(v4.count(marker) == 1, 'ambiguous canonical source marker')
        start = v4.index(marker) + len(marker)
        raw = v4[start:start + row['bytes']]
        endmarker = b'\n\n# END CANONICAL SOURCE: ' + name.encode() + b'\n'
        require(v4[start + len(raw):].startswith(endmarker), 'canonical source terminator differs')
        require(sha(raw) == row['sha256'], 'canonical source bytes differ: ' + name)
        blocks[name] = raw
        observations.append({**row, 'bundle_start_byte': start, 'hash_verified': True})
    return blocks, observations

def derive_spans(v4: bytes) -> list[dict]:
    """Re-derive the frozen inventory's heading spans, including its preambles.

The original inventory treats hash-prefixed headings inside fenced examples as
section boundaries. Preserve that exact scope; do not relabel these as 328 clauses.
"""
    lines = v4.decode('utf-8').splitlines(keepends=True)
    result, src, start, heading = [], None, None, None
    for pos, line in enumerate(lines):
        if line.startswith('# BEGIN CANONICAL SOURCE: '):
            require(src is None, 'nested canonical block')
            src, start, heading = line.strip().split(': ', 1)[1], pos + 1, 'SOURCE_PREAMBLE'
        elif src is not None and (line.startswith('# END CANONICAL SOURCE: ') or
                                   re.match(r'^#{1,6} ', line)):
            if any(x.strip() for x in lines[start:pos]):
                raw = ''.join(lines[start:pos]).encode()
                entry = {'source': src, 'heading': heading, 'start_line': start + 1,
                         'end_line': pos, 'span_sha256': sha(raw)}
                entry['span_id'] = 'S-' + sha(canonical(entry))[:24]
                result.append(entry)
            if line.startswith('# END CANONICAL SOURCE: '):
                require(line.strip().split(': ', 1)[1] == src, 'wrong source end marker')
                src, start, heading = None, None, None
            else:
                start, heading = pos, line.strip()
    require(src is None and len(result) == 328, 'expected complete 328-span inventory')
    require(len({r['span_id'] for r in result}) == 328, 'duplicate derived span identity')
    return result

def verify_source_map(mapping: dict, spans: list[dict], v4: bytes) -> None:
    require(mapping.get('source_sha256') == sha(v4), 'preservation map V4 pin differs')
    rows = mapping.get('source_sections')
    require(isinstance(rows, list) and len(rows) == 328, 'source map incomplete')
    keys = ('source', 'heading', 'start_line', 'end_line', 'span_sha256')
    for row, derived in zip(rows, spans):
        require(same({k: row.get(k) for k in keys}, {k: derived[k] for k in keys}),
                'map coverage/range/identity differs')
        require(row.get('destination') == V5_NAME and row.get('disposition') == 'RETAIN',
                'map destination/disposition differs')
    # semantic_equivalence/status/resolved metadata is deliberately NOT consulted.

def verify_v5_bundle(data: dict[str, bytes], v4: bytes) -> tuple[int, list[dict]]:
    idx = load_json(data['SEMANTIC_INPUTS_R08.json'])
    entries = idx.get('files')
    require(isinstance(entries, list) and len(entries) == 35, 'V5 input inventory incomplete')
    expected = ['frozen-source/' + V4_NAME, 'VPL_V5_EXECUTION_BIBLE_R10.md',
                'VPL_V5_REMEDIATION_BIBLE_R05.md', 'O1_ADOPTED_AMENDMENTS.md']
    expected += sorted(n for n in data if n.startswith('controls/') and n.count('/') == 1
                       and PurePosixPath(n).suffix in ('.json', '.md'))
    expected += ['templates/BUILD_RECAP_STRUCTURE_R02.html']
    require([e['path'] for e in entries] == expected, 'V5 source set/order differs')
    require(idx.get('no_selective_pruning') is True and idx.get('runtime_ingestion_certified') is False,
            'invalid semantic source scope')
    require(idx.get('module_ids') == [f'M{i:02}' for i in range(1, 27)], 'module scope differs')
    bundle = data[V5_NAME]
    first = b'\n\n## Source 1: '
    require(bundle.count(first) == 1, 'ambiguous first V5 source')
    header = bundle.split(first, 1)[0]
    require(b'Published authority remains V4 FINAL / REV006.' in header and
            b'On an unresolved conflict: STOP for source/owner resolution.' in header,
            'V5 authority/conflict boundary missing')
    rebuilt = header
    for i, e in enumerate(entries, 1):
        path = valid_path(e['path'])
        require(path in data and sha(data[path]) == e['sha256'] and len(data[path]) == e['size_bytes'],
                'V5 semantic entry hash/size differs: ' + path)
        rebuilt += (f'\n\n## Source {i}: {path}\n\nSHA-256: {e["sha256"]}\n\n'
                    '<!-- BEGIN PINNED SOURCE -->\n').encode() + data[path] + b'\n<!-- END PINNED SOURCE -->\n'
    require(rebuilt == bundle, 'V5 bundle cannot be reconstructed from complete source list')
    require(data['frozen-source/' + V4_NAME] == v4 and bundle.count(v4) == 1,
            'recovered V4 authority not uniquely verbatim in V5')
    return bundle[:bundle.index(v4)].count(b'\n'), entries

def field_diffs(old: dict, new: dict) -> list[dict]:
    """Keep missing and JSON null distinct, and preserve complete values."""
    return [{'field': k, 'source_present': k in old, 'destination_present': k in new,
             'source_value': old.get(k), 'destination_value': new.get(k)}
            for k in sorted(set(old) | set(new))
            if k not in old or k not in new or not same(old[k], new[k])]

def registry_comparison(blocks: dict, data: dict) -> dict:
    rows, wrappers = [], []
    for source, dest, array in REGISTRIES:
        a, b = load_json(blocks[source]), load_json(data[dest])
        def keyed(x):
            require(isinstance(x.get(array), list) and len(x[array]) > 0, 'missing registry rows')
            out = {}
            for r in x[array]:
                require(isinstance(r, dict) and isinstance(r.get('id'), str), 'invalid registry row')
                require(r['id'] not in out, 'duplicate registry ID')
                out[r['id']] = r
            return out
        aa, bb = keyed(a), keyed(b)
        require(set(aa) == set(bb), 'registry IDs added/removed without mapping')
        for rid in sorted(aa):
            diffs = field_diffs(aa[rid], bb[rid])
            rows.append({'id': rid, 'source_file': source, 'destination_file': dest,
                         'array': array, 'source_value': aa[rid], 'destination_value': bb[rid],
                         'source_payload_sha256': sha(canonical(aa[rid])),
                         'destination_payload_sha256': sha(canonical(bb[rid])),
                         'comparison': 'NONIDENTICAL_REQUIRES_RULE' if diffs else 'IDENTICAL_ROW_VALUE',
                         'field_differences': diffs})
        wrappers.append({'source_file': source, 'destination_file': dest,
                         'field_differences': field_diffs({k: v for k, v in a.items() if k != array},
                                                        {k: v for k, v in b.items() if k != array}),
                         'interpretation': 'V4 wrapper remains inherited; V5 wrapper is not an identical replacement. Model approval required.'})
    return {'rows': rows, 'wrapper_context_differences': wrappers,
            'total_registry_rows': len(rows),
            'identical_row_values': sum(r['comparison'] == 'IDENTICAL_ROW_VALUE' for r in rows),
            'changed_row_values': sum(r['comparison'] != 'IDENTICAL_ROW_VALUE' for r in rows)}

def bounded_translation_checks(reg: dict, data: dict) -> dict:
    """Execute explicit PROPOSED source-to-destination predicate mappings.

    These predicates are not approved semantic rules merely because they pass.
    M01/M25 are exact transformations, not similarity tests. Persistence pairs
    make the human interpretation assumption visible with exact source anchors.
    Scheduler replacement is reported as a change, never an identity proof.
    """
    by_id = {r['id']: r for r in reg['rows']}
    checks = []
    m01 = by_id['M01']
    old, new = m01['source_value']['contract'], m01['destination_value']['contract']
    a = 'REV 006 retains'
    b = 'V4 FINAL / REV006 authority also requires'
    suffix = ' Candidate mutable position is read only from VPL_V5_CURRENT_STATE.json.'
    checks.append({'id': 'TR-M01', 'operator': 'EXACT_NAMED_SUBSTITUTION_AND_APPEND',
                   'source_id': 'M01', 'source_phrase': a, 'destination_phrase': b,
                   'append': suffix, 'predicate_pass': old.count(a) == 1 and new == old.replace(a,b) + suffix,
                   'semantic_adjudication': 'REQUIRED_NOT_INFERRED'})
    m25 = by_id['M25']
    suffix = ' A trigger while this module remains proposed must return UNCERTAIN_ESCALATE and STOP; owner activation is separate.'
    checks.append({'id': 'TR-M25', 'operator': 'EXACT_APPEND_WITH_ORIGINAL_CONTRACT_UNCHANGED',
                   'source_id': 'M25', 'append': suffix,
                   'predicate_pass': m25['destination_value']['contract'] == m25['source_value']['contract'] + suffix,
                   'semantic_adjudication': 'REQUIRED_NOT_INFERRED'})
    current = data['controls/PERSISTENCE_STATE_R02.md'].decode('utf-8')
    pairs = [
        ('M26', 'contract', 'Load all canonical V4 module semantics equally', 'loaded the full equal-priority semantic set', 'equal-priority full load'),
        ('M26', 'contract', 'balanced anti-parroting challenge plus behavioral proof', 'required balanced behavioral challenge', 'balanced challenge'),
        ('M26', 'contract', 'save complete receipt', 'banked a matching durable receipt with readback', 'durable source-bound receipt'),
        ('VPL-GL-15', 'requirement', 'Load every canonical V4 operating control with equal persistence priority', 'loaded the full equal-priority semantic set', 'equal-priority full load'),
        ('VPL-GL-15', 'requirement', 'balanced challenge coverage and immediate behavioral test', 'required balanced behavioral challenge', 'balanced challenge plus action-bound contract'),
        ('VPL-GL-15', 'requirement', 'Self-report, recap or selective retrieval is not evidence', 'a verbal recap, or a contract-test PASS alone cannot establish CURRENT', 'recap/test PASS insufficient'),
    ]
    for i, (rid, field, left, right, concept) in enumerate(pairs, 1):
        checks.append({'id': f'PR-{i:02}', 'operator': 'PROPOSED_LITERAL_ANCHORED_PROPOSITION_PAIR',
                       'concept': concept, 'source_id': rid, 'source_field': field,
                       'source_anchor': left, 'destination_file': 'controls/PERSISTENCE_STATE_R02.md',
                       'destination_anchor': right,
                       'predicate_pass': left in by_id[rid]['source_value'][field] and right in current,
                       'semantic_adjudication': 'PAIR_MEANING_REQUIRES_APPROVAL; matching anchors is not a general entailment proof'})
    a01, m26 = by_id['VPL-A01'], by_id['M26']
    scheduler_change = (a01['source_value'].get('schedule') != a01['destination_value'].get('schedule'))
    module_change = ('Hostinger 15-minute full-bundle producer' in m26['source_value']['contract'] and
                     'optional freshness/watchdog producer' in m26['destination_value']['contract'])
    return {'checks': checks, 'predicates_passed': sum(x['predicate_pass'] for x in checks),
            'predicates_total': len(checks),
            'cron_policy_difference_detected': scheduler_change and module_change,
            'cron_policy_classification': 'NONIDENTICAL_DESIGN_REQUIRES_EXPLICIT_OWNER_DISPOSITION',
            'rule_status': 'PROPOSED_FOR_SEMANTIC_ADJUDICATION_NOT_SELF_APPROVED'}


def verify_rules(rules: dict, spans: list[dict], offset: int, reg: dict,
                 matrix: bytes, entries: list[dict]) -> list[dict]:
    require(isinstance(rules, dict), 'rule model must be an object')
    require(rules.get('kind') == 'ACTION1_SOURCE_BOUND_RULE_MODEL_R01' and
            rules.get('rule_id') == MODEL_RULE, 'unknown rule model')
    require(rules.get('proof_scope') == 'BOUNDED_SPECIFICATION_MODEL_NOT_RUNTIME', 'scope overclaim')
    require(rules.get('v4_sha256') == V4_SHA and rules.get('candidate_sha256') == CANDIDATE_SHA,
            'rule source binding differs')
    require(rules.get('matrix_sha256') == sha(matrix), 'rule matrix binding differs')
    require(same(rules.get('v5_source_inventory'), entries), 'rule overlay inventory differs')
    require(same(rules.get('registry_comparison'), reg), 'rule registry values/context differ')
    maps = rules.get('span_mappings')
    require(isinstance(maps, list) and len(maps) == len(spans), 'rule mappings incomplete')
    out = []
    for expected, row in zip(spans, maps):
        require(isinstance(row, dict), 'span mapping must be an object')
        require(same(row.get('source_span'), expected), 'mapping source span altered or reordered')
        require(row.get('operator') == 'EXACT_SPAN_IN_COMPLETE_SOURCE_CONTEXT', 'unknown span operator')
        destination = {'file': V5_NAME, 'start_line': expected['start_line'] + offset,
                       'end_line': expected['end_line'] + offset, 'span_sha256': expected['span_sha256']}
        require(same(row.get('destination_span'), destination), 'mapping destination wrong')
        require(row.get('manual') == 'LOAD_ALL_PRESERVE_SOURCE_DEFINED_TRIGGERS' and
                row.get('light') == 'LOAD_ALL_PRESERVE_SOURCE_DEFINED_TRIGGERS', 'route/activation assumption changed')
        require(row.get('approval_requirement') == 'EXTERNAL_MODEL_ADJUDICATION', 'missing approval boundary')
        out.append({'span_id': expected['span_id'], 'source': expected,
                    'destination': destination, 'operator': row['operator'],
                    'manual': row['manual'], 'light': row['light'],
                    'mechanical_result': 'PASS_VERBATIM_SPECIFICATION_MAPPING',
                    'semantic_status': 'UNRESOLVED',
                    'reason': 'Exact-span/context predicate passes; this does not approve the inheritance/overlay semantic model.'})
    return out

def check_adjudication(raw: bytes | None, trusted_sha: str | None, rules_sha: str,
                       reg: dict) -> tuple[bool, dict]:
    """External trust input, not a signature, reviewer identity, or self-approval.

A future operator must supply BOTH an approval record and its independently
trusted SHA. This wave supplies no such record. Synthetic fixtures are tests only.
"""
    if raw is None:
        require(trusted_sha is None, 'approval hash without record')
        return False, {'state': 'NOT_SUPPLIED', 'identity_attested': False}
    require(trusted_sha is not None and bool(re.fullmatch('[0-9a-f]{64}', trusted_sha)) and
            sha(raw) == trusted_sha, 'untrusted approval record')
    a = load_json(raw)
    require(isinstance(a, dict), 'approval must be an object')
    require(a.get('kind') == 'ACTION1_EXTERNAL_SPECIFICATION_MODEL_ADJUDICATION_R01', 'wrong approval kind')
    require(a.get('rules_sha256') == rules_sha and a.get('candidate_sha256') == CANDIDATE_SHA and
            a.get('v4_sha256') == V4_SHA, 'approval binds different source/model')
    require(a.get('decision') == 'APPROVE_BOUNDED_SPECIFICATION_MODEL' and
            a.get('rule_id') == MODEL_RULE, 'no explicit semantic model approval')
    require(a.get('all_328_span_mappings_reviewed') is True and
            a.get('all_35_source_overlay_effects_reviewed') is True and
            a.get('wrapper_context_differences_reviewed') is True, 'incomplete interpretation review')
    for key in ('owner_decision_reference', 'independent_review_reference'):
        require(isinstance(a.get(key), str) and bool(a[key].strip()), 'missing approval provenance')
    ids = sorted(r['id'] for r in reg['rows'] if r['field_differences'])
    require(a.get('approved_changed_registry_ids') == ids, 'changed registry decisions incomplete')
    return True, {'state': 'SUPPLIED_HASH_PINNED_DECLARATION', 'sha256': sha(raw),
                  'owner_decision_reference': a['owner_decision_reference'],
                  'independent_review_reference': a['independent_review_reference'],
                  'identity_attested': False,
                  'trust_boundary': 'Supplied trusted hash and documentary declarations; authenticity/semantic judgment are external, not proved by this program.'}

def evaluate(candidate: bytes, authority: dict[str, bytes], rules_raw: bytes,
             adjudication: bytes | None = None, adjudication_sha: str | None = None) -> dict:
    require(sha(candidate) == CANDIDATE_SHA, 'candidate archive pin mismatch')
    data = read_archive(candidate)
    require(len(data) == 451 and git_tree(data) == CANDIDATE_TREE, 'candidate count/tree differs')
    for name, pin in AUTH_PINS.items():
        require(name in authority and sha(authority[name]) == pin, 'recovered authority pin mismatch: ' + name)
    v4 = authority[V4_NAME]
    manifest = load_json(authority['VPL_V4_EQUAL_LOAD_MANIFEST_REV006.json'])
    challenge = load_json(authority['PERSISTENCE_CHALLENGE_MATRIX_REV006.json'])
    sums = {line.split('  ', 1)[1]: line.split('  ', 1)[0]
            for line in authority['REV006_SHA256SUMS.txt'].decode().splitlines()}
    for name in (V4_NAME, 'VPL_V4_EQUAL_LOAD_MANIFEST_REV006.json', 'PERSISTENCE_CHALLENGE_MATRIX_REV006.json'):
        require(sums.get(name) == sha(authority[name]), 'recovered file fails original checksum record')
    blocks, block_proofs = source_blocks(v4, manifest)
    spans = derive_spans(v4)
    mapping = load_json(data[MAP])
    verify_source_map(mapping, spans, v4)
    offset, entries = verify_v5_bundle(data, v4)
    matrix = data[MATRIX]
    require(mapping.get('matrix_sha256') == sha(matrix), 'matrix/map hash mismatch')
    base_ids = re.findall(rb'^\| ((?:P|M|L|C)\d{2}) \|', matrix, re.M)
    h_ids = re.findall(rb'^\| (H\d{2}) \|', matrix, re.M)
    require(len(base_ids) == len(set(base_ids)) == 41 and h_ids == [f'H{i:02}'.encode() for i in range(1,8)], 'matrix rows missing/duplicated')
    reg = registry_comparison(blocks, data)
    rules = load_json(rules_raw)
    results = verify_rules(rules, spans, offset, reg, matrix, entries)
    translation = bounded_translation_checks(reg, data)
    require(same(rules.get('bounded_translation_checks'), translation), 'translation predicate mapping differs')
    require(translation['predicates_passed'] == translation['predicates_total'], 'bounded translation predicate failed')
    v4_lines = v4.splitlines(keepends=True)
    v5_lines = data[V5_NAME].splitlines(keepends=True)
    for result in results:
        a, b = result['source'], result['destination']
        aa = b''.join(v4_lines[a['start_line']-1:a['end_line']])
        bb = b''.join(v5_lines[b['start_line']-1:b['end_line']])
        require(aa == bb and sha(aa) == a['span_sha256'], 'actual mapped span differs')
    # Evaluate authoritative machine-readable persistence predicates, without
    # pretending a schema/challenge definition is an executed behavioral proof.
    require(manifest.get('no_priority_weighting') is True, 'equal-priority invariant missing')
    require(challenge.get('mutation_requirement') == 'PERSISTENCE_STATE=CURRENT', 'mutation invariant missing')
    require(challenge.get('proofs') == ['semantic_completeness', 'cross_reference_understanding',
                                      'false_premise_rejection', 'behavioral_follow_through'], 'proof requirement differs')
    approved, approval = check_adjudication(adjudication, adjudication_sha, sha(rules_raw), reg)
    if approved:
        for result in results:
            result['semantic_status'] = 'EQUIVALENT_UNDER_EXTERNALLY_APPROVED_BOUNDED_MODEL'
            result['reason'] = 'Derived exact-span/context predicate passed under the hash-pinned external model adjudication; no runtime claim.'
    missing = [name for name in ('VPL_V4_EQUAL_LOAD_MANIFEST_REV006.json', 'PERSISTENCE_CHALLENGE_MATRIX_REV006.json')
               if not any(PurePosixPath(p).name == name for p in data)]
    return {'kind': 'SEMANTIC_EQUIVALENCE_RECEIPT_R08_ACTION1_R01',
            'result': 'PASS_UNDER_EXTERNALLY_APPROVED_BOUNDED_MODEL' if approved else 'MECHANICAL_PASS_SEMANTIC_APPROVAL_PENDING',
            'exit_code': 0 if approved else 3,
            'total_spans': len(results), 'mechanically_verified_spans': len(results),
            'resolved': len(results) if approved else 0,
            'unresolved': 0 if approved else len(results),
            'r2_m01_closed': False, 'closure_note': 'Independent coordinator/owner closure is external; this tool never promotes the finding.',
            'proof_scope': 'BOUNDED_SPECIFICATION_MODEL_NOT_RUNTIME',
            'verifier_sha256': sha(Path(__file__).read_bytes()),
            'source_binding': {'candidate_archive_sha256': sha(candidate), 'candidate_git_tree': git_tree(data),
                               'candidate_files': len(data), 'v4_bundle_sha256': sha(v4),
                               'v5_bundle_sha256': sha(data[V5_NAME]), 'matrix_sha256': sha(matrix),
                               'rule_model_sha256': sha(rules_raw)},
            'source_blocks': block_proofs, 'v5_source_inventory': entries,
            'matrix_base_rows_verified': 41, 'matrix_h_rows_verified': 7,
            'registry_comparison': reg, 'bounded_translation_checks': translation, 'external_model_adjudication': approval,
            'recovered_authority_files': [{'file': n, 'sha256': sha(authority[n]),
                                          'original_checksum_matches': True} for n in AUTH_PINS if n != 'REV006_SHA256SUMS.txt'],
            'supplemented_original_candidate_omissions': missing,
            'supplement_note': 'Recovered V4 authority accompanies the sidecar; missing originals were NOT inserted into or backdated into frozen candidate.',
            'persistence_definition_checks': {'equal_priority': True, 'complete_29_sources': True,
                                            'balanced_four_proofs_required': True, 'runtime_proofs_executed': False},
            'results': results,
            'zero_manual_decisions_during_execution': True,
            'rule_authorship_and_semantic_adjudication_are_not_automated': True,
            'arbitrary_natural_language_conflicts_exhaustively_detected': False,
            'runtime_or_model_persistence_certified': False, 'candidate_code_executed': False,
            'source_written': False, 'score_change_established': False}

def main(argv=None) -> int:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument('--candidate', type=Path, required=True)
    parser.add_argument('--v4-authority', type=Path, required=True)
    parser.add_argument('--rules', type=Path, required=True)
    parser.add_argument('--output', type=Path, required=True)
    parser.add_argument('--adjudication', type=Path)
    parser.add_argument('--adjudication-sha256')
    args = parser.parse_args(argv)
    try:
        require(not args.output.exists(), 'refusing to overwrite existing output')
        require(args.output.parent.is_dir(), 'output directory must already exist')
        authority = {n: (args.v4_authority/n).read_bytes() for n in AUTH_PINS}
        receipt = evaluate(args.candidate.read_bytes(), authority, args.rules.read_bytes(),
                           args.adjudication.read_bytes() if args.adjudication else None,
                           args.adjudication_sha256)
        with args.output.open('xb') as f:
            f.write(canonical(receipt))
        print(json.dumps({'result': receipt['result'], 'total_spans': receipt['total_spans'],
                          'mechanically_verified_spans': receipt['mechanically_verified_spans'],
                          'resolved': receipt['resolved'], 'unresolved': receipt['unresolved'],
                          'receipt_sha256': sha(args.output.read_bytes())}, sort_keys=True))
        return receipt['exit_code']
    except (InvalidInput, OSError, KeyError, TypeError, IndexError) as e:
        print(json.dumps({'result': 'INPUT_REJECTED', 'error': str(e)}, sort_keys=True), file=sys.stderr)
        return 2

if __name__ == '__main__':
    sys.exit(main())
action3/check_t01_resume.py

14,677 bytes · SHA-256 92d436e1ebb17411186d764d81d6883daa35e455264b6096553641c39d45b861

Download exact source file

#!/usr/bin/env python3
"""Bounded T01 confirmation harness; only disposable output directories are written.
Uses real source preflight and runner execution, never journal-supplied executable code.
ACTION3 R01 integrates a test-only source-frame stall for --timeout-only. The fault
uses a real subprocess timeout; normal execution and final resume are uninjected.
No production/runtime certification or semantic-equivalence proof is implied.
"""
from __future__ import annotations
import argparse,copy,hashlib,importlib.util,json,math,os,runpy,shutil,subprocess,sys,time,types
sys.dont_write_bytecode = True
from pathlib import Path
from datetime import datetime,timezone

def sha(p):return hashlib.sha256(Path(p).read_bytes()).hexdigest()
def write(p,d):Path(p).write_text(json.dumps(d,indent=2)+'\n')

# ACTION3 R01: test-only fault injection, never a candidate-source patch.
# The real selected source frame is held before its first statement. The parent
# waits for that handshake before starting a real subprocess timeout. Startup
# speed and the runtime of unrelated commands therefore cannot select the fault.

def positive_seconds(value):
 value=float(value)
 if not math.isfinite(value) or value <= 0:
  raise argparse.ArgumentTypeError('timing must be finite and positive')
 return value

def nonnegative_seconds(value):
 value=float(value)
 if not math.isfinite(value) or value < 0:
  raise argparse.ArgumentTypeError('delay must be finite and nonnegative')
 return value

def wait_for_source_frame(proc,ready,target,limit):
 deadline=time.monotonic()+limit
 while not ready.exists():
  code=proc.poll()
  if code is not None:
   raise RuntimeError('fault child exited before source-frame handshake: '+str(code))
  if time.monotonic() >= deadline:
   raise RuntimeError('source-frame handshake deadline exceeded; NOT a verified timeout')
  time.sleep(0.01)
 event=json.loads(ready.read_text())
 if (event.get('kind')!='ACTION3_SOURCE_FRAME_STALL' or
     event.get('pid')!=proc.pid or event.get('target')!=str(target) or
     event.get('target_sha256')!=sha(target)):
  raise RuntimeError('invalid source-frame handshake')
 return event

def run_with_source_stall(command,ready,timeout_seconds,ready_seconds,startup_delay,**kwargs):
 """Run actual source through a test-only entry-frame gate; raise real timeout."""
 if not isinstance(command,list) or len(command)<3 or command[1]!='-B':
  raise ValueError('unexpected source command shape')
 target=Path(command[2]).resolve()
 if ready.exists():
  raise ValueError('refusing existing fault evidence')
 gated=[sys.executable,'-B',str(Path(__file__).resolve()),'--action3-gated-child',
        str(ready),str(startup_delay),str(target),*command[3:]]
 kwargs.pop('timeout',None)  # Start fault budget ONLY after the handshake.
 proc=subprocess.Popen(gated,**kwargs)
 try:
  wait_for_source_frame(proc,ready,target,ready_seconds)
  # This is the real subprocess wait, not a fabricated exception or exit code.
  code=proc.wait(timeout=timeout_seconds)
  return subprocess.CompletedProcess(command,code)
 finally:
  if proc.poll() is None:
   proc.kill()
  proc.wait()

def gated_child(argv):
 ready=Path(argv[0]);delay=nonnegative_seconds(argv[1]);target=Path(argv[2]).resolve()
 if ready.exists():
  raise ValueError('ready path already exists')
 time.sleep(delay)  # Optional explicit slow-start regression fixture.
 sys.argv=[str(target),*argv[3:]];sys.path.insert(0,str(target.parent))
 def at_source(frame,event,arg):
  if event=='call' and frame.f_code.co_filename==str(target):
   body={'kind':'ACTION3_SOURCE_FRAME_STALL','pid':os.getpid(),
         'target':str(target),'target_sha256':sha(target),
         'phase':'before_first_statement','test_only':True}
   temp=ready.with_suffix('.pending');write(temp,body);temp.replace(ready)
   print('ACTION3 TEST-ONLY STALL: '+str(target),flush=True)
   while True:time.sleep(3600)  # Parent kills this process on its bounded timeout.
  return at_source
 sys.settrace(at_source)
 try:runpy.run_path(str(target),run_name='__main__')
 finally:sys.settrace(None)
 raise RuntimeError('target frame was never intercepted')

def subprocess_proxy(run):
 return types.SimpleNamespace(**dict(vars(subprocess),run=run))

def fault_runner(argv):
 script=Path(argv[0]).resolve();index=int(argv[1]);ready=Path(argv[2]).resolve()
 budget=positive_seconds(argv[3]);startup=positive_seconds(argv[4]);delay=nonnegative_seconds(argv[5])
 sys.path.insert(0,str(script.parent))
 spec=importlib.util.spec_from_file_location('action3_frozen_runner',script)
 module=importlib.util.module_from_spec(spec);spec.loader.exec_module(module)
 plan=module.command_plan()
 if not 1<=index<=len(plan):raise ValueError('fault index outside actual plan')
 target_name=plan[index-1][0]
 if sum(name==target_name for name,_ in plan)!=1:raise ValueError('ambiguous fault target')
 injected=[]
 def controlled_run(command,**kwargs):
  if len(command)>=3 and Path(command[2]).name==target_name:
   if injected:raise RuntimeError('duplicate injection attempt')
   injected.append(target_name)
   return run_with_source_stall(command,ready,budget,startup,delay,**kwargs)
  return subprocess.run(command,**kwargs)
 # Change only this imported runner's subprocess binding for this fault run.
 # Do not patch the global subprocess module, source files, plan, hashes or receipts.
 module.subprocess=subprocess_proxy(controlled_run)
 sys.argv=[str(script),*argv[6:]]
 code=module.main()
 if not injected:raise RuntimeError('requested fault was not reached')
 return code

def validate_timeout_checkpoint(seed,journal,failure,index):
 """Derived state checks: no completed work may be credited to a failed attempt."""
 checks=[
  (failure.get('result')=='FAIL','missing failed reproduction receipt'),
  (journal.get('commands')==seed.get('commands'),'verified checkpoint advanced or changed'),
  (len(journal.get('attempts',[]))==len(seed.get('attempts',[]))+1,'unexpected attempt count'),
  (journal.get('state')=='FAILED_OR_INCOMPLETE','journal not marked failed/incomplete'),
 ]
 for passed,reason in checks:
  if not passed:raise AssertionError(reason)
 attempt=journal['attempts'][-1]
 if (attempt.get('index')!=index or attempt.get('exit_code')!=124 or
     attempt.get('state')!='FAILED_OR_INCOMPLETE'):
  raise AssertionError('requested command did not record a genuine incomplete timeout')
 return attempt

def main():
 if sys.flags.optimize:raise RuntimeError('Run without -O / PYTHONOPTIMIZE; assertions must be enabled')
 p=argparse.ArgumentParser(description=__doc__);p.add_argument('--candidate',type=Path,required=True);p.add_argument('--archive',type=Path,required=True);p.add_argument('--out',type=Path,required=True);p.add_argument('--actor',required=True);p.add_argument('--session',required=True);p.add_argument('--timeout-only',action='store_true');p.add_argument('--fault-at',type=int,default=3);p.add_argument('--fault-timeout',type=positive_seconds,default=0.2);p.add_argument('--fault-ready-timeout',type=positive_seconds,default=30.0);p.add_argument('--fault-startup-delay',type=nonnegative_seconds,default=0.0);p.add_argument('--runner-timeout',type=positive_seconds,default=600.0);a=p.parse_args()
 if not 2<=a.fault_at<=12:p.error('--fault-at must be between 2 and 12')
 C=a.candidate.resolve();A=a.archive.resolve();O=a.out.resolve()
 if O.exists():raise ValueError('Output must be new; preserve previous evidence')
 if O==C or O.is_relative_to(C) or C.is_relative_to(O):raise ValueError('Source/output must be disjoint')
 O.mkdir(parents=True);results=[];run=O/'legitimate'
 initial={q.relative_to(C).as_posix():sha(q) for q in C.rglob('*') if q.is_file()}
 script=C/'runner/verify_review_packet.py'
 def invoke(label,out,extra=(),actor=None,archive=None,expected=0,fault=False):
  log=O/(label+'.log');cmd=[sys.executable,'-B',str(script),'--actor',actor or a.actor,'--session',a.session,'--out',str(out),'--predecessor-archive',str(archive or A),*extra]
  if fault:
   cmd=[sys.executable,'-B',str(Path(__file__).resolve()),'--action3-fault-runner',str(script),str(a.fault_at),str(O/'FAULT_READY.json'),str(a.fault_timeout),str(a.fault_ready_timeout),str(a.fault_startup_delay),*cmd[3:]]
  start=time.monotonic()
  with log.open('wb') as f:r=subprocess.run(cmd,stdout=f,stderr=subprocess.STDOUT,timeout=a.runner_timeout)
  record={'id':label,'exit_code':r.returncode,'expected_exit_code':expected,'pass':r.returncode==expected,'log':log.name,'log_sha256':sha(log),'seconds':round(time.monotonic()-start,3)}
  results.append(record);write(O/'CHECKS.json',results);print(json.dumps(record),flush=True)
  if not record['pass']:raise AssertionError(label+' returned unexpected exit code')
  return log
 if a.timeout_only:
  checkpoint=a.fault_at-1
  invoke('pause_after_'+str(checkpoint),run,['--stop-after',str(checkpoint)],expected=75)
  seed=json.loads((run/'COMMAND_PROGRESS.json').read_text())
  assert len(seed['commands'])==checkpoint
  invoke('forced_timeout',run,['--resume'],expected=1,fault=True)
  failure_path=run/'REVIEWER_REPRODUCTION_RECEIPT.json'
  failure=json.loads(failure_path.read_text())
  journal=json.loads((run/'COMMAND_PROGRESS.json').read_text())
  # A failed attempt must not advance the verified checkpoint or invent a PASS.
  failed_attempt=validate_timeout_checkpoint(seed,journal,failure,a.fault_at)
  ready=json.loads((O/'FAULT_READY.json').read_text())
  target=Path(ready['target']);assert target.name==seed['plan'][a.fault_at-1][0]
  assert ready['target_sha256']==sha(C/'runner'/target.name)
  failure_bytes=failure_path.read_bytes();failure_hash=sha(failure_path)
  invoke('resume_after_timeout',run,['--resume'],expected=0)
  receipt=json.loads(failure_path.read_text())
  assert (run/'receipt_history'/f'{failure_hash}.json').read_bytes()==failure_bytes
  journal=json.loads((run/'COMMAND_PROGRESS.json').read_text())
  assert journal['commands'][:checkpoint]==seed['commands']
  assert failed_attempt in journal['attempts']
  assert len(journal['attempts'])==len(seed['plan'])+1
  assert [row['index'] for row in journal['commands']]==list(range(1,len(seed['plan'])+1))
  assert receipt['resumptions'][-1]['completed_commands_restored']==checkpoint
  results.append({'id':'failed_attempt_and_receipt_preserved','pass':True,
                  'failed_receipt_sha256':failure_hash,'fault_at_command':a.fault_at,
                  'completed_commands_restored':checkpoint,
                  'fault_mode':'test-only source-frame stall; real subprocess timeout',
                  'fault_timeout_seconds':a.fault_timeout,
                  'startup_handshake_timeout_seconds':a.fault_ready_timeout,
                  'configured_startup_delay_seconds':a.fault_startup_delay,
                  'fault_ready_sha256':sha(O/'FAULT_READY.json'),
                  'resume_mode':'unmodified real runner; no injection'})
 else:
  invoke('pause_after_9',run,['--stop-after','9'],expected=75)
  seed=json.loads((run/'COMMAND_PROGRESS.json').read_text());assert len(seed['commands'])==9
  # These are not copies of a real user edit. They intentionally forge journals in disposable fixtures.
  for kind in ('replace','add','delete'):
   dest=O/('tampered_'+kind);shutil.copytree(run,dest)
   journal=copy.deepcopy(seed);target='runner/t01_added_fixture.py' if kind=='add' else 'runner/test_hf03.py'
   if kind=='delete':delta={'path':target,'deleted':True}
   else:
    artifact=dest/'forged_fixture.py';artifact.write_text("raise RuntimeError('T01 FORGED FIXTURE MUST NOT EXECUTE')\n")
    delta={'path':target,'artifact':artifact.name,'sha256':sha(artifact)}
   journal['commands'][-1]['delta'].append(delta);write(dest/'COMMAND_PROGRESS.json',journal)
   before=len(journal['attempts']);log=invoke('reject_'+kind,dest,['--resume'],expected=2)
   assert 'delta outside writable evidence scope: '+target in log.read_text()
   after=json.loads((dest/'COMMAND_PROGRESS.json').read_text());assert len(after['attempts'])==before
   results.append({'id':kind+'_rejected_before_next_execution','pass':True,'target':target})
  wrong=O/'wrong_actor';shutil.copytree(run,wrong);invoke('wrong_actor',wrong,['--resume'],actor=a.actor+'-OTHER',expected=2)
  wrong=O/'wrong_archive';shutil.copytree(run,wrong);bad=O/'incorrect_archive.zip';bad.write_bytes(b'wrong-archive-fixture');invoke('wrong_archive',wrong,['--resume'],archive=bad,expected=2)
  # Valid restart must discard an unrecorded mutation, then execute only remaining commands.
  (run/'isolated-candidate/runner/unrecorded_interruption.py').write_text('# abandoned scratch mutation\n')
  invoke('legitimate_resume',run,['--resume'],expected=0)
  assert not (run/'isolated-candidate/runner/unrecorded_interruption.py').exists()
  receipt=json.loads((run/'REVIEWER_REPRODUCTION_RECEIPT.json').read_text());journal=json.loads((run/'COMMAND_PROGRESS.json').read_text())
  assert [x['index'] for x in journal['commands']]==list(range(1,13)) and len(journal['attempts'])==12
  assert receipt['resumptions'][-1]['completed_commands_restored']==9
  results.append({'id':'exactly_remaining_commands_executed','pass':True,'restored':9,'executed_after_resume':[10,11,12]})
 assert receipt['result']=='PASS' and receipt['recounted_tests']['failed']==receipt['recounted_tests']['not_run']==0
 exact=[]
 for row in receipt['result_comparisons']:
  output=run/'reports'/Path(row['path']).name
  equal=output.read_bytes()==(C/row['path']).read_bytes();assert equal
  exact.append({'path':row['path'],'sha256':sha(output),'byte_identical':equal,'recounted':row['recounted']})
 final={q.relative_to(C).as_posix():sha(q) for q in C.rglob('*') if q.is_file()};assert initial==final
 write(O/'CHECKS.json',results)
 report={'kind':'T01_FORCED_INTERRUPTION_CONFIRMATION' if a.timeout_only else 'T01_REAL_RUNNER_RESUME_CONFIRMATION','result':'PASS','actor':a.actor,'session':a.session,'at_utc':datetime.now(timezone.utc).isoformat(),'source_files':len(initial),'source_unchanged':True,'state_sha256':sha(C/'VPL_V5_CURRENT_STATE.json'),'reproduction_receipt_sha256':sha(run/'REVIEWER_REPRODUCTION_RECEIPT.json'),'commands':len(receipt['commands']),'recounted':receipt['recounted_tests'],'reports':exact,'checks':results,'boundary':'Executing actor evidence only. Not independent reviewer acceptance, authenticated identity, provider or production certification.'}
 write(O/'SUMMARY.json',report);print(json.dumps({'result':report['result'],'recounted':report['recounted'],'reports':len(exact)}),flush=True)
if __name__=='__main__':
 if len(sys.argv)>1 and sys.argv[1]=='--action3-gated-child':gated_child(sys.argv[2:])
 elif len(sys.argv)>1 and sys.argv[1]=='--action3-fault-runner':sys.exit(fault_runner(sys.argv[2:]))
 else:main()
action3/check_t01_resume.py.diff

14,311 bytes · SHA-256 effd3dc40d78da47455f65daee188d4c0c45d752f7f8cbc2a08a79db7e33c304

Download exact source file

--- frozen-handoff/check_t01_resume.py
+++ ACTION3_TIMEOUT_R01/source/check_t01_resume.py
@@ -1,40 +1,190 @@
 #!/usr/bin/env python3
 """Bounded T01 confirmation harness; only disposable output directories are written.
 Uses real source preflight and runner execution, never journal-supplied executable code.
+ACTION3 R01 integrates a test-only source-frame stall for --timeout-only. The fault
+uses a real subprocess timeout; normal execution and final resume are uninjected.
+No production/runtime certification or semantic-equivalence proof is implied.
 """
 from __future__ import annotations
-import argparse,copy,hashlib,json,os,shutil,subprocess,sys,time
+import argparse,copy,hashlib,importlib.util,json,math,os,runpy,shutil,subprocess,sys,time,types
+sys.dont_write_bytecode = True
 from pathlib import Path
 from datetime import datetime,timezone
 
 def sha(p):return hashlib.sha256(Path(p).read_bytes()).hexdigest()
 def write(p,d):Path(p).write_text(json.dumps(d,indent=2)+'\n')
+
+# ACTION3 R01: test-only fault injection, never a candidate-source patch.
+# The real selected source frame is held before its first statement. The parent
+# waits for that handshake before starting a real subprocess timeout. Startup
+# speed and the runtime of unrelated commands therefore cannot select the fault.
+
+def positive_seconds(value):
+ value=float(value)
+ if not math.isfinite(value) or value <= 0:
+  raise argparse.ArgumentTypeError('timing must be finite and positive')
+ return value
+
+def nonnegative_seconds(value):
+ value=float(value)
+ if not math.isfinite(value) or value < 0:
+  raise argparse.ArgumentTypeError('delay must be finite and nonnegative')
+ return value
+
+def wait_for_source_frame(proc,ready,target,limit):
+ deadline=time.monotonic()+limit
+ while not ready.exists():
+  code=proc.poll()
+  if code is not None:
+   raise RuntimeError('fault child exited before source-frame handshake: '+str(code))
+  if time.monotonic() >= deadline:
+   raise RuntimeError('source-frame handshake deadline exceeded; NOT a verified timeout')
+  time.sleep(0.01)
+ event=json.loads(ready.read_text())
+ if (event.get('kind')!='ACTION3_SOURCE_FRAME_STALL' or
+     event.get('pid')!=proc.pid or event.get('target')!=str(target) or
+     event.get('target_sha256')!=sha(target)):
+  raise RuntimeError('invalid source-frame handshake')
+ return event
+
+def run_with_source_stall(command,ready,timeout_seconds,ready_seconds,startup_delay,**kwargs):
+ """Run actual source through a test-only entry-frame gate; raise real timeout."""
+ if not isinstance(command,list) or len(command)<3 or command[1]!='-B':
+  raise ValueError('unexpected source command shape')
+ target=Path(command[2]).resolve()
+ if ready.exists():
+  raise ValueError('refusing existing fault evidence')
+ gated=[sys.executable,'-B',str(Path(__file__).resolve()),'--action3-gated-child',
+        str(ready),str(startup_delay),str(target),*command[3:]]
+ kwargs.pop('timeout',None)  # Start fault budget ONLY after the handshake.
+ proc=subprocess.Popen(gated,**kwargs)
+ try:
+  wait_for_source_frame(proc,ready,target,ready_seconds)
+  # This is the real subprocess wait, not a fabricated exception or exit code.
+  code=proc.wait(timeout=timeout_seconds)
+  return subprocess.CompletedProcess(command,code)
+ finally:
+  if proc.poll() is None:
+   proc.kill()
+  proc.wait()
+
+def gated_child(argv):
+ ready=Path(argv[0]);delay=nonnegative_seconds(argv[1]);target=Path(argv[2]).resolve()
+ if ready.exists():
+  raise ValueError('ready path already exists')
+ time.sleep(delay)  # Optional explicit slow-start regression fixture.
+ sys.argv=[str(target),*argv[3:]];sys.path.insert(0,str(target.parent))
+ def at_source(frame,event,arg):
+  if event=='call' and frame.f_code.co_filename==str(target):
+   body={'kind':'ACTION3_SOURCE_FRAME_STALL','pid':os.getpid(),
+         'target':str(target),'target_sha256':sha(target),
+         'phase':'before_first_statement','test_only':True}
+   temp=ready.with_suffix('.pending');write(temp,body);temp.replace(ready)
+   print('ACTION3 TEST-ONLY STALL: '+str(target),flush=True)
+   while True:time.sleep(3600)  # Parent kills this process on its bounded timeout.
+  return at_source
+ sys.settrace(at_source)
+ try:runpy.run_path(str(target),run_name='__main__')
+ finally:sys.settrace(None)
+ raise RuntimeError('target frame was never intercepted')
+
+def subprocess_proxy(run):
+ return types.SimpleNamespace(**dict(vars(subprocess),run=run))
+
+def fault_runner(argv):
+ script=Path(argv[0]).resolve();index=int(argv[1]);ready=Path(argv[2]).resolve()
+ budget=positive_seconds(argv[3]);startup=positive_seconds(argv[4]);delay=nonnegative_seconds(argv[5])
+ sys.path.insert(0,str(script.parent))
+ spec=importlib.util.spec_from_file_location('action3_frozen_runner',script)
+ module=importlib.util.module_from_spec(spec);spec.loader.exec_module(module)
+ plan=module.command_plan()
+ if not 1<=index<=len(plan):raise ValueError('fault index outside actual plan')
+ target_name=plan[index-1][0]
+ if sum(name==target_name for name,_ in plan)!=1:raise ValueError('ambiguous fault target')
+ injected=[]
+ def controlled_run(command,**kwargs):
+  if len(command)>=3 and Path(command[2]).name==target_name:
+   if injected:raise RuntimeError('duplicate injection attempt')
+   injected.append(target_name)
+   return run_with_source_stall(command,ready,budget,startup,delay,**kwargs)
+  return subprocess.run(command,**kwargs)
+ # Change only this imported runner's subprocess binding for this fault run.
+ # Do not patch the global subprocess module, source files, plan, hashes or receipts.
+ module.subprocess=subprocess_proxy(controlled_run)
+ sys.argv=[str(script),*argv[6:]]
+ code=module.main()
+ if not injected:raise RuntimeError('requested fault was not reached')
+ return code
+
+def validate_timeout_checkpoint(seed,journal,failure,index):
+ """Derived state checks: no completed work may be credited to a failed attempt."""
+ checks=[
+  (failure.get('result')=='FAIL','missing failed reproduction receipt'),
+  (journal.get('commands')==seed.get('commands'),'verified checkpoint advanced or changed'),
+  (len(journal.get('attempts',[]))==len(seed.get('attempts',[]))+1,'unexpected attempt count'),
+  (journal.get('state')=='FAILED_OR_INCOMPLETE','journal not marked failed/incomplete'),
+ ]
+ for passed,reason in checks:
+  if not passed:raise AssertionError(reason)
+ attempt=journal['attempts'][-1]
+ if (attempt.get('index')!=index or attempt.get('exit_code')!=124 or
+     attempt.get('state')!='FAILED_OR_INCOMPLETE'):
+  raise AssertionError('requested command did not record a genuine incomplete timeout')
+ return attempt
+
 def main():
- p=argparse.ArgumentParser(description=__doc__);p.add_argument('--candidate',type=Path,required=True);p.add_argument('--archive',type=Path,required=True);p.add_argument('--out',type=Path,required=True);p.add_argument('--actor',required=True);p.add_argument('--session',required=True);p.add_argument('--timeout-only',action='store_true');a=p.parse_args()
+ if sys.flags.optimize:raise RuntimeError('Run without -O / PYTHONOPTIMIZE; assertions must be enabled')
+ p=argparse.ArgumentParser(description=__doc__);p.add_argument('--candidate',type=Path,required=True);p.add_argument('--archive',type=Path,required=True);p.add_argument('--out',type=Path,required=True);p.add_argument('--actor',required=True);p.add_argument('--session',required=True);p.add_argument('--timeout-only',action='store_true');p.add_argument('--fault-at',type=int,default=3);p.add_argument('--fault-timeout',type=positive_seconds,default=0.2);p.add_argument('--fault-ready-timeout',type=positive_seconds,default=30.0);p.add_argument('--fault-startup-delay',type=nonnegative_seconds,default=0.0);p.add_argument('--runner-timeout',type=positive_seconds,default=600.0);a=p.parse_args()
+ if not 2<=a.fault_at<=12:p.error('--fault-at must be between 2 and 12')
  C=a.candidate.resolve();A=a.archive.resolve();O=a.out.resolve()
  if O.exists():raise ValueError('Output must be new; preserve previous evidence')
  if O==C or O.is_relative_to(C) or C.is_relative_to(O):raise ValueError('Source/output must be disjoint')
  O.mkdir(parents=True);results=[];run=O/'legitimate'
  initial={q.relative_to(C).as_posix():sha(q) for q in C.rglob('*') if q.is_file()}
  script=C/'runner/verify_review_packet.py'
- def invoke(label,out,extra=(),actor=None,archive=None,expected=0):
-  log=O/(label+'.log');cmd=[sys.executable,'-B',str(script),'--actor',actor or a.actor,'--session',a.session,'--out',str(out),'--predecessor-archive',str(archive or A),*extra];start=time.time()
-  with log.open('wb') as f:r=subprocess.run(cmd,stdout=f,stderr=subprocess.STDOUT,timeout=600)
-  record={'id':label,'exit_code':r.returncode,'expected_exit_code':expected,'pass':r.returncode==expected,'log':log.name,'log_sha256':sha(log),'seconds':round(time.time()-start,3)}
+ def invoke(label,out,extra=(),actor=None,archive=None,expected=0,fault=False):
+  log=O/(label+'.log');cmd=[sys.executable,'-B',str(script),'--actor',actor or a.actor,'--session',a.session,'--out',str(out),'--predecessor-archive',str(archive or A),*extra]
+  if fault:
+   cmd=[sys.executable,'-B',str(Path(__file__).resolve()),'--action3-fault-runner',str(script),str(a.fault_at),str(O/'FAULT_READY.json'),str(a.fault_timeout),str(a.fault_ready_timeout),str(a.fault_startup_delay),*cmd[3:]]
+  start=time.monotonic()
+  with log.open('wb') as f:r=subprocess.run(cmd,stdout=f,stderr=subprocess.STDOUT,timeout=a.runner_timeout)
+  record={'id':label,'exit_code':r.returncode,'expected_exit_code':expected,'pass':r.returncode==expected,'log':log.name,'log_sha256':sha(log),'seconds':round(time.monotonic()-start,3)}
   results.append(record);write(O/'CHECKS.json',results);print(json.dumps(record),flush=True)
   if not record['pass']:raise AssertionError(label+' returned unexpected exit code')
   return log
  if a.timeout_only:
-  invoke('pause_after_2',run,['--stop-after','2'],expected=75)
-  invoke('forced_timeout',run,['--resume','--command-timeout','1'],expected=1)
-  failure=json.loads((run/'REVIEWER_REPRODUCTION_RECEIPT.json').read_text());assert failure['result']=='FAIL'
-  journal=json.loads((run/'COMMAND_PROGRESS.json').read_text());assert len(journal['commands'])==2 and journal['attempts'][-1]['exit_code']==124
-  failure_hash=sha(run/'REVIEWER_REPRODUCTION_RECEIPT.json')
+  checkpoint=a.fault_at-1
+  invoke('pause_after_'+str(checkpoint),run,['--stop-after',str(checkpoint)],expected=75)
+  seed=json.loads((run/'COMMAND_PROGRESS.json').read_text())
+  assert len(seed['commands'])==checkpoint
+  invoke('forced_timeout',run,['--resume'],expected=1,fault=True)
+  failure_path=run/'REVIEWER_REPRODUCTION_RECEIPT.json'
+  failure=json.loads(failure_path.read_text())
+  journal=json.loads((run/'COMMAND_PROGRESS.json').read_text())
+  # A failed attempt must not advance the verified checkpoint or invent a PASS.
+  failed_attempt=validate_timeout_checkpoint(seed,journal,failure,a.fault_at)
+  ready=json.loads((O/'FAULT_READY.json').read_text())
+  target=Path(ready['target']);assert target.name==seed['plan'][a.fault_at-1][0]
+  assert ready['target_sha256']==sha(C/'runner'/target.name)
+  failure_bytes=failure_path.read_bytes();failure_hash=sha(failure_path)
   invoke('resume_after_timeout',run,['--resume'],expected=0)
-  receipt=json.loads((run/'REVIEWER_REPRODUCTION_RECEIPT.json').read_text())
-  assert (run/'receipt_history'/f'{failure_hash}.json').is_file()
-  journal=json.loads((run/'COMMAND_PROGRESS.json').read_text());assert any(x.get('exit_code')==124 for x in journal['attempts'])
-  results.append({'id':'failed_attempt_and_receipt_preserved','pass':True,'failed_receipt_sha256':failure_hash,'completed_commands_restored':receipt['resumptions'][-1]['completed_commands_restored']})
+  receipt=json.loads(failure_path.read_text())
+  assert (run/'receipt_history'/f'{failure_hash}.json').read_bytes()==failure_bytes
+  journal=json.loads((run/'COMMAND_PROGRESS.json').read_text())
+  assert journal['commands'][:checkpoint]==seed['commands']
+  assert failed_attempt in journal['attempts']
+  assert len(journal['attempts'])==len(seed['plan'])+1
+  assert [row['index'] for row in journal['commands']]==list(range(1,len(seed['plan'])+1))
+  assert receipt['resumptions'][-1]['completed_commands_restored']==checkpoint
+  results.append({'id':'failed_attempt_and_receipt_preserved','pass':True,
+                  'failed_receipt_sha256':failure_hash,'fault_at_command':a.fault_at,
+                  'completed_commands_restored':checkpoint,
+                  'fault_mode':'test-only source-frame stall; real subprocess timeout',
+                  'fault_timeout_seconds':a.fault_timeout,
+                  'startup_handshake_timeout_seconds':a.fault_ready_timeout,
+                  'configured_startup_delay_seconds':a.fault_startup_delay,
+                  'fault_ready_sha256':sha(O/'FAULT_READY.json'),
+                  'resume_mode':'unmodified real runner; no injection'})
  else:
   invoke('pause_after_9',run,['--stop-after','9'],expected=75)
   seed=json.loads((run/'COMMAND_PROGRESS.json').read_text());assert len(seed['commands'])==9
@@ -71,4 +221,7 @@
  write(O/'CHECKS.json',results)
  report={'kind':'T01_FORCED_INTERRUPTION_CONFIRMATION' if a.timeout_only else 'T01_REAL_RUNNER_RESUME_CONFIRMATION','result':'PASS','actor':a.actor,'session':a.session,'at_utc':datetime.now(timezone.utc).isoformat(),'source_files':len(initial),'source_unchanged':True,'state_sha256':sha(C/'VPL_V5_CURRENT_STATE.json'),'reproduction_receipt_sha256':sha(run/'REVIEWER_REPRODUCTION_RECEIPT.json'),'commands':len(receipt['commands']),'recounted':receipt['recounted_tests'],'reports':exact,'checks':results,'boundary':'Executing actor evidence only. Not independent reviewer acceptance, authenticated identity, provider or production certification.'}
  write(O/'SUMMARY.json',report);print(json.dumps({'result':report['result'],'recounted':report['recounted'],'reports':len(exact)}),flush=True)
-if __name__=='__main__':main()
+if __name__=='__main__':
+ if len(sys.argv)>1 and sys.argv[1]=='--action3-gated-child':gated_child(sys.argv[2:])
+ elif len(sys.argv)>1 and sys.argv[1]=='--action3-fault-runner':sys.exit(fault_runner(sys.argv[2:]))
+ else:main()
action4/dedup_ledger.py

16,536 bytes · SHA-256 8fd6fbe4d62dae365051ecabbabcfafc03508a93ec584d47bf790bb7d5fcec3f

Download exact source file

#!/usr/bin/env python3
"""Deterministic, preservation-first pre-ingestion gate for the VPL R05 intake.

Standard-library only. No model, network, scoring, semantic matching or source edits.
Exact payload equality is NOT proof of shared authorship or independent execution.
Original IDs/rows/provenance remain in every emitted ledger. Nothing is deleted.
"""
from __future__ import annotations
import argparse
import copy
import hashlib
import json
import re
import sys
from collections import defaultdict
from pathlib import Path
from typing import Any

POLICY_ID = 'ACTION4_EXACT_PAYLOAD_PREINGEST_R01'
SLOTS = frozenset({'CLAUDE', 'GEMINI', 'GROK', 'DEEPSEEK', 'GLM', 'KIMI'})
COLLECTIONS = {
    'new_findings': 'NEW_FINDING',
    'prior_finding_dispositions': 'PRIOR_DISPOSITION',
    'prior_dispositions': 'PRIOR_DISPOSITION',
    'prior_n_rows': 'PRIOR_DISPOSITION',
    'independently_confirmed_peer_limitations': 'PEER_CONFIRMATION',
}
CATEGORIES = ('NEW_FINDING', 'PRIOR_DISPOSITION', 'PEER_CONFIRMATION')
# For NEW findings only, the identifier is excluded from grouping. Prior/peer
# reference identities remain part of the group key. In particular: retain
# severity, disposition, evidence, location, stage, all prose and list ordering.
ID_FIELDS = frozenset({'id', 'finding_id'})

class AdmissionError(ValueError):
    """Malformed/ambiguous input must not create a publishable derived ledger."""


def require(condition: bool, message: str) -> None:
    if not condition:
        raise AdmissionError(message)


def canonical(value: Any) -> bytes:
    """This project's JSON serialization, not a claim of universal JSON canonicalization."""
    try:
        return json.dumps(value, sort_keys=True, ensure_ascii=False,
                          separators=(',', ':'), allow_nan=False).encode('utf-8')
    except (TypeError, ValueError, UnicodeError) as exc:
        raise AdmissionError(f'NON_CANONICAL_JSON: {exc}') from exc


def sha(data: bytes) -> str:
    return hashlib.sha256(data).hexdigest()


def strict_json(data: bytes) -> Any:
    def pairs(items):
        result = {}
        for key, val in items:
            require(key not in result, f'DUPLICATE_JSON_KEY: {key}')
            result[key] = val
        return result
    def bad_constant(value):
        raise AdmissionError(f'NON_FINITE_JSON: {value}')
    try:
        return json.loads(data.decode('utf-8'), object_pairs_hook=pairs,
                          parse_constant=bad_constant)
    except (UnicodeError, json.JSONDecodeError) as exc:
        raise AdmissionError(f'INVALID_JSON: {exc}') from exc


def nonblank(value: Any, field: str) -> str:
    require(isinstance(value, str) and bool(value.strip()), f'INVALID_{field}')
    return value


def row_id(row: dict) -> str:
    present = [row[k] for k in ID_FIELDS if k in row]
    require(bool(present), 'MISSING_FINDING_ID')
    for value in present:
        nonblank(value, 'FINDING_ID')
    require(len(set(present)) == 1, 'CONFLICTING_ID_ALIASES')
    return present[0]


def source_context(intake: dict) -> dict:
    source = intake.get('source')
    require(isinstance(source, dict), 'MISSING_SOURCE_CONTEXT')
    repo = nonblank(source.get('repository'), 'REPOSITORY')
    commit = source.get('commit')
    require(isinstance(commit, str) and re.fullmatch('[0-9a-f]{40}', commit) is not None,
            'INVALID_SOURCE_COMMIT')
    return {'repository': repo, 'commit': commit}


def collect_rows(intake: dict) -> tuple[list[dict], list[dict], dict]:
    require(isinstance(intake, dict), 'INTAKE_NOT_OBJECT')
    require(intake.get('kind') == 'V5_FRESH_R03_CONSOLIDATED_INTAKE', 'WRONG_INTAKE_KIND')
    require(isinstance(intake.get('reviewer_records'), dict), 'MISSING_REVIEWER_RECORDS')
    require(set(intake['reviewer_records']) == SLOTS, 'REVIEWER_COVERAGE_MISMATCH')
    context = source_context(intake)
    raw_records, coverage = [], []
    for slot in sorted(SLOTS):
        record = intake['reviewer_records'][slot]
        require(isinstance(record, dict), f'INVALID_REVIEWER_RECORD: {slot}')
        require('submitted_ledger' in record, f'MISSING_LEDGER_FIELD: {slot}')
        ledger = record['submitted_ledger']
        if ledger is None:
            reason = nonblank(record.get('ledger_availability'), 'MISSING_LEDGER_REASON')
            coverage.append({'slot': slot, 'ledger_state': 'NOT_AVAILABLE',
                             'reason': reason, 'rows': None, 'not_a_clean_review': True})
            continue
        require(isinstance(ledger, dict), f'INVALID_LEDGER: {slot}')
        require(ledger.get('reviewer_slot') == slot, f'REVIEWER_NAMESPACE_MISMATCH: {slot}')
        require('new_findings' in ledger, f'MISSING_NEW_FINDINGS: {slot}')
        target = ledger.get('target')
        if target is not None:
            require(isinstance(target, dict), f'INVALID_TARGET: {slot}')
            if 'source_commit' in target:
                require(target['source_commit'] == context['commit'], f'MIXED_SOURCE_COMMIT: {slot}')
            if 'repository' in target:
                require(target['repository'] == context['repository'], f'MIXED_REPOSITORY: {slot}')
        # Reject unrecognized finding/disposition row arrays, rather than ignore them.
        for name, value in ledger.items():
            if isinstance(value, list) and any(t in name.lower() for t in ('finding', 'disposition')):
                require(name in COLLECTIONS, f'UNSUPPORTED_ROW_COLLECTION: {slot}/{name}')
        counts, seen_identity = defaultdict(int), {}
        for collection, category in COLLECTIONS.items():
            if collection not in ledger:
                continue
            array = ledger[collection]
            require(isinstance(array, list), f'INVALID_ARRAY: {slot}/{collection}')
            for index, row in enumerate(array):
                require(isinstance(row, dict), f'INVALID_ROW: {slot}/{collection}/{index}')
                identifier = row_id(row)
                payload = {k: v for k, v in row.items() if k not in ID_FIELDS}
                require(bool(payload), f'EMPTY_FINDING_PAYLOAD: {slot}/{identifier}')
                if category == 'NEW_FINDING':
                    require(any(isinstance(row.get(k), str) and row[k].strip() for k in ('title', 'finding', 'description')),
                            f'MISSING_FINDING_CLAIM: {slot}/{identifier}')
                payload_bytes = canonical(payload)
                identity = (category, identifier)
                if identity in seen_identity:
                    require(seen_identity[identity] == payload_bytes,
                            f'CONFLICTING_ID_WITHIN_REVIEWER: {slot}/{identifier}')
                seen_identity[identity] = payload_bytes
                pointer = f'/reviewer_records/{slot}/submitted_ledger/{collection}/{index}'
                raw_records.append({
                    'occurrence_ref': pointer, 'slot': slot, 'category': category,
                    'collection': collection, 'original_id': identifier,
                    'original_row': copy.deepcopy(row),
                    'original_row_canonical_sha256': sha(canonical(row)),
                    'payload_sha256': sha(payload_bytes),
                    'source_context': context.copy(),
                    'provenance': {
                        'round': record.get('round'),
                        'source_review_commit': record.get('source_review_commit'),
                        'stage1_commit': record.get('stage1_commit'),
                        'declared_session': ledger.get('session', ledger.get('audit_run_id')),
                        'remote_ledger_sha256_as_recorded': record.get('ledger_sha256', record.get('ledger_git_sha256')),
                        'embedded_ledger_canonical_sha256': sha(canonical(ledger)),
                        'assurance': 'BOUND_TO_INPUT_SNAPSHOT_NOT_NEW_PROVIDER_ATTESTATION',
                    },
                })
                counts[category] += 1
        coverage.append({'slot': slot, 'ledger_state': 'PRESENT',
                         'rows': sum(counts.values()), 'by_category': dict(sorted(counts.items())),
                         'intake_status_as_recorded': record.get('intake_status')})
    require(bool(raw_records), 'NO_ROWS_TO_INGEST')
    return raw_records, coverage, context


def deduplicate_before_ingestion(raw_records: list[dict]) -> tuple[list[dict], list[dict]]:
    """Group before counting. Compare actual bytes as well as their digest.

    Never merge by title/topic, reviewer name, source ID alone, supplied duplicate
    flags, or inferred meaning. A different evidence reference changes the payload.
    """
    buckets = defaultdict(list)
    for item in raw_records:
        payload = {k: v for k, v in item['original_row'].items() if k not in ID_FIELDS}
        # A generic 'OPEN' disposition on N01 is not the disposition on N02.
        # Ignore ID only for substantive NEW_FINDING payload copies, never for
        # references/dispositions about an identified pre-existing finding.
        key = canonical({'category': item['category'], 'source': item['source_context'],
                         'referenced_id': None if item['category'] == 'NEW_FINDING' else item['original_id'],
                         'payload': payload})
        buckets[key].append(item)
    groups, crosswalk = [], []
    for key in sorted(buckets):
        members = sorted(buckets[key], key=lambda x: x['occurrence_ref'])
        group_id = 'A4-' + sha(key)
        # Representative is a storage choice, explicitly not a human authorship claim.
        rep = members[0]
        ids = sorted({x['original_id'] for x in members})
        group = {
            'group_id': group_id, 'category': rep['category'],
            'payload_sha256': rep['payload_sha256'],
            'payload': {k: v for k, v in rep['original_row'].items() if k not in ID_FIELDS},
            'representative_ref': rep['occurrence_ref'],
            'representative_is_not_authorship_claim': True,
            'occurrences': [x['occurrence_ref'] for x in members],
            'source_ids': ids, 'reviewer_slots': sorted({x['slot'] for x in members}),
            'duplicate_occurrences_excluded_from_new_content_count': len(members) - 1,
            'additional_independent_votes_from_repetition': 0,
            'independent_reproduction_status': 'NOT_ESTABLISHED_BY_DEDUPLICATION',
            'content_admission_only': True,
        }
        groups.append(group)
        for i, row in enumerate(members):
            crosswalk.append({
                'occurrence_ref': row['occurrence_ref'], 'slot': row['slot'],
                'original_id': row['original_id'], 'category': row['category'],
                'group_id': group_id,
                'content_count_contribution': 1 if i == 0 else 0,
                'classification': 'REPRESENTATIVE_CONTENT' if i == 0 else 'EXACT_PAYLOAD_REPEAT',
                'duplicate_additional_independent_vote_credit': 0,
                'raw_record_retained': True,
            })
    return sorted(groups, key=lambda x: x['group_id']), sorted(crosswalk, key=lambda x: x['occurrence_ref'])


def build_ledger(intake: dict, input_sha256: str) -> dict:
    require(isinstance(input_sha256, str) and re.fullmatch('[0-9a-f]{64}', input_sha256) is not None,
            'INVALID_INPUT_HASH')
    # Validate the entire preserved JSON tree, including otherwise uncounted metadata.
    canonical(intake)
    raw, coverage, context = collect_rows(intake)
    groups, crosswalk = deduplicate_before_ingestion(raw)
    # No tally is accepted before the gate: counts come ONLY from admitted groups.
    counts = {}
    for category in CATEGORIES:
        rows_n = sum(r['category'] == category for r in raw)
        groups_n = sum(g['category'] == category for g in groups)
        counts[category] = {'submitted_rows': rows_n, 'distinct_payload_groups': groups_n,
                            'repeat_occurrences_not_added_to_content_count': rows_n - groups_n}
    require(len(crosswalk) == len(raw), 'CROSSWALK_COVERAGE_ERROR')
    require(sum(x['content_count_contribution'] for x in crosswalk) == len(groups), 'COUNT_ERROR')
    # Same IDs with different conclusions across reviewers are retained as variants.
    variants = defaultdict(list)
    for item in raw:
        variants[(item['category'], item['original_id'])].append(item)
    id_variants = []
    for (category, identifier), members in sorted(variants.items()):
        if len({x['payload_sha256'] for x in members}) > 1:
            id_variants.append({'category': category, 'source_id': identifier,
                                'status': 'DISTINCT_PAYLOADS_PRESERVED_NOT_ADJUDICATED',
                                'occurrences': sorted(x['occurrence_ref'] for x in members)})
    return {
        'kind': 'VPL_ACTION4_PREINGESTED_LEDGER', 'policy_id': POLICY_ID,
        'input_sha256': input_sha256, 'input_revision': intake.get('revision'),
        'source_context': context,
        'scope': 'Reprocess historical FRESH_R03 inputs that fed HF03; no findings re-reviewed or closed.',
        'gate': 'EXACT_PAYLOAD_DEDUPLICATION_BEFORE_DERIVED_LEDGER_COUNTS',
        'rules': {
            'new_finding_id_fields_excluded_from_payload_comparison': sorted(ID_FIELDS),
            'prior_and_peer_reference_ids_included_in_group_key': True,
            'canonicalization': 'JSON sorted object keys, UTF-8, compact separators; no prose/list normalization',
            'all_other_payload_fields_compared': True,
            'supplied_duplicate_flags_used_as_proof': False,
            'similarity_or_id_only_merge': False,
            'source_truth_or_authorship_verified': False,
            'automatic_independent_corroboration_award': False,
        },
        'counts': counts, 'reviewer_coverage': coverage,
        'raw_records': raw, 'admitted_groups': groups, 'duplicate_crosswalk': crosswalk,
        'same_id_distinct_payload_variants': id_variants,
        'original_intake': copy.deepcopy(intake),
        'status': 'DERIVED_CONTENT_LEDGER_NOT_REVIEW_VERDICT_OR_SCORE',
        'limitations': [
            'Exact payload comparison does not detect paraphrase/semantic duplicates.',
            'Distinct payloads are not necessarily distinct underlying defects.',
            'Distinct evidence references remain separate; their truth/independence requires evidence review.',
            'Repeated identical payloads supply zero additional independent votes automatically.',
            'The bound consolidated intake is the input; no fresh Git/remote reviewer revalidation is implied.',
            'Missing ledger is recorded as unavailable, never as a clean review.',
            'No frozen candidate, historical disposition, assessment, or live pipeline is overwritten.',
        ],
    }


def serialize(value: Any) -> bytes:
    return (json.dumps(value, sort_keys=True, ensure_ascii=False, indent=2, allow_nan=False) + '\n').encode('utf-8')


def main(argv: list[str] | None = None) -> int:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument('--input', type=Path, required=True)
    parser.add_argument('--expected-input-sha256', required=True)
    parser.add_argument('--output', type=Path, required=True,
                        help='New output file only; existing files are never overwritten.')
    args = parser.parse_args(argv)
    try:
        require(not args.input.is_symlink(), 'INPUT_SYMLINK_NOT_ALLOWED')
        data = args.input.read_bytes()
        require(len(data) <= 20_000_000, 'INPUT_TOO_LARGE')
        require(sha(data) == args.expected_input_sha256, 'INPUT_HASH_MISMATCH')
        document = build_ledger(strict_json(data), sha(data))
        require(args.output.resolve() != args.input.resolve(), 'OUTPUT_IS_INPUT')
        require(args.output.parent.is_dir(), 'OUTPUT_PARENT_MISSING')
        encoded = serialize(document)  # finish serialization before creating any output
        # Exclusive create prevents accidental replacement of prior evidence.
        with args.output.open('xb') as stream:
            stream.write(encoded)
        print(json.dumps({'status': 'DERIVED_LEDGER_WRITTEN', 'counts': document['counts'],
                          'output_sha256': sha(args.output.read_bytes()), 'policy': POLICY_ID}, sort_keys=True))
        return 0
    except (AdmissionError, OSError, RecursionError) as exc:
        print(f'ADMISSION_STOP: {exc}', file=sys.stderr)
        return 2

if __name__ == '__main__':
    raise SystemExit(main())

Download original source manifest

These source artifacts support bounded specification review, test-only timeout/recovery checks and exact-payload grouping. Their presence does not establish production safety, unmocked Guard 2, autonomous state transport or an installed CURRENT state.

Read Final Assessment · Gemini report correction R02

The evaluator-issued report below retains the accepted 95.00 / 99.50 scores and corrects their reasoning and comparability statements. It is preserved as an assessment record, not a new test run or an operating instruction.

Download original assessment report

# GEMINI_REPORT_CORRECTION_ONLY_R02

**Delivery and arithmetic:** VERIFIED.  
**Evaluator-issued successor scores:** 95.00 / 99.50 (Retained unchanged).

## 1. Rubric, Evidence, and Traceability Corrections

This report amends the justifications, classifications, and traceability of the R01 final grading to strictly align with the `GRADING_READSET_R01.md` evidence boundaries. No scoring formulas or totals have been altered.

### A. Track 2, Dimension E (Product/System Integration) Correction
*   **Original R01 Flaw:** Incorrectly labeled Dimension E as "production execution" and justified the score retention (9.5/10) based purely on the lack of MySmartRouter production evidence.
*   **Corrected Judgment:** The V3 authority defines Dimension E as "Product / System Integration," expressly excluding deployment status from direct scoring weight. The 9.5/10 score stands *not* because of a deployment gap, but because the new HF03/T01 evidence (Actions 1, 3, 4) is entirely framework-focused. It introduces test-only fault harnesses and ledger scripts but does not provide new architectural evidence regarding product integration design for MySmartRouter or MyFlightWatcher. The score is carried forward accurately; the rationale is corrected to reflect the V3 rubric's scope.

### B. Track 1, Dimension 8 (QA/Debugging/Recovery) Correction
*   **Original R01 Flaw:** Assumed 97/100 was a mathematical ceiling and that the +0.50 score cap was forced by "strict points limits."
*   **Corrected Judgment:** A score of 97/100 is not a hard cap. The retention of 97 is an evidence-based judgment. Action 3 introduced 13 focused test methods utilizing a test-only deterministic timeout harness. While this proves advanced diagnostic isolation (a local subprocess proxy for fault injection), it does *not* prove unmocked Guard 2 execution or live production recovery. A score of 97 already reflects elite QA/Debugging capability. Retaining it acknowledges that Action 3 was a diagnostic sidecar repair that restored local state exactly, but it did not fundamentally expand the architecture's QA scope beyond what the 97 already captured.

### C. Track 2, Dimensions B & D Correction
*   **Original R01 Flaw:** Awarded the remaining +0.5 headroom without addressing the original historical deductions (synchronous HTTP vs. event streaming; sequential dispatch vs. autonomous collaboration).
*   **Corrected Judgment:** The original product architectures (HTTP, sequential dispatch) were not altered by HF03/T01. However, the +0.5 gains are still warranted under the V3 rubric because V3 scores *applied-AI architect capability*, not just specific product feature implementation. Action 1 (mechanically mapped semantics and owner amendments) demonstrates mastery over deterministic state enforcement (System Architecture, Dim B), mitigating risks inherent in synchronous patterns. Action 4 (exact-payload duplicate pruning before ingestion) demonstrates elite control over multi-model outputs (Agent Orchestration, Dim D), proving advanced boundary management despite the lack of fully autonomous collaboration. The score gains are affirmed based on framework maturation demonstrating high-level architectural judgment.

### D. Action 1 & Action 4 Evidence Boundaries Corrected
*   **Action 1:** The R01 report overstated Action 1's effect. Action 1 yields `ADEQUATE_WITH_STATED_LIMITATIONS`. It mechanically mapped spans and applied explicit A01/M26 owner amendments, but it does *not* constitute unqualified R2-M01 closure or establish a runtime CURRENT state. It proves bounded specification acceptance, leaving owner judgment as an active necessity.
*   **Action 4:** The R01 report falsely claimed the baseline suffered from "finding-count inflation." The evidence shows Action 4 processed 210 *mixed* records (including dispositions and peer confirmations), intentionally grouping content without awarding independent votes to duplicates. The score gain reflects the sophisticated mechanical handling of these payloads, not the correction of an inflated previous score.
*   **Live Connectors:** The assertion "No live API, Git, or Drive connectors" is corrected. The bounded sidecar tests (Actions 1, 3, 4) did not require or verify *new* connector operations. A coordinator Drive transfer occurred, but this does not prove automated state transport or grant new integration score credit.

---

## 2. Track 1: HF03/T01 10-Dimension Assessment (Corrected)

| Dimension | Baseline | Successor | Delta | New Supporting Evidence | Limiting Evidence |
| :--- | :---: | :---: | :---: | :--- | :--- |
| **1. AI/System Architecture** | 95 | 96 | +1 | Action 1: Mechanical verification of 328 spans and A01/M26 explicit owner amendment. | Action 1 yields ADEQUATE_WITH_STATED_LIMITATIONS; Q1 M26 encoding limits remain. |
| **2. Problem Decomposition** | 95 | 96 | +1 | Action 4: Exact-payload pre-ingestion sidecar handles 210 mixed rows into 106 groups. | Duplicate identification previously existed manually; this is mechanical grouping. |
| **3. Multi-Model Orchestration** | 94 | 95 | +1 | Action 4: Retains exact provenance while grouping multi-model output without independent vote inflation. | Continued manual courier overhead limits orchestration fluidity. |
| **4. Product/System Integration** | 92 | 92 | 0 | (Carry Forward) | No new MySmartRouter product integration evidence provided. |
| **5. API/Data/Tool Integration** | 92 | 92 | 0 | (Carry Forward) | No new live Drive/Git connector operation verified in these sidecars. |
| **6. Workflow & State Architecture** | 95 | 96 | +1 | Action 3: Deterministic timeout harness decouples state checks from machine speed. | State transport still requires human courier execution. |
| **7. Governance & Change Control** | 93 | 94 | +1 | Action 1: R04 adjudication replaces manual trust with mechanically verified specification models. | Guard 2 unmocked execution remains unverified; cooperative identity binding. |
| **8. QA/Debugging/Recovery** | 97 | 97 | 0 | Action 3: 13 focused tests successfully restored exact states via local subprocess proxy. | Test-only fault injection; does not constitute live production recovery. |
| **9. Operational Problem Translation** | 95 | 95 | 0 | (Carry Forward) | No new evidence specific to operational business translation. |
| **10. Individual AI Leverage** | 97 | 97 | 0 | (Carry Forward) | Leverage bounded by lack of live agent autonomous execution. |

**Track 1 Total:** 950 / 10 = **95.00**

---

## 3. Track 2: V3 9-Dimension Weighted Assessment (Corrected)

| Dimension | Baseline | Successor | Delta | Reason for Change |
| :--- | :---: | :---: | :---: | :--- |
| **A. Problem Decomposition & Op Translation** | 14.5 | 15.0 | +0.5 | Action 4: Mechanical handling of 210 mixed records into grouped architectures demonstrates elite decomposition capability. |
| **B. AI / System Architecture** | 14.5 | 15.0 | +0.5 | Action 1: Mechanical verification of semantic spans proves deterministic control, mitigating risks of original synchronous HTTP deductions. |
| **C. Human Architectural Judgment & Control** | 14.5 | 15.0 | +0.5 | Action 1: R04 adjudication and explicit owner amendments demonstrate precise human governance over AI models. |
| **D. AI Agent Orchestration & Leverage** | 11.5 | 12.0 | +0.5 | Action 4: Grouping cross-model outputs without vote inflation proves advanced boundary management, offsetting original sequential dispatch deductions. |
| **E. Product / System Integration** | 9.5 | 9.5 | 0 | Unchanged: No new evidence provided for product-level (MySmartRouter) system integration. |
| **F. Workflow, State & Recovery Architecture** | 9.5 | 10.0 | +0.5 | Action 3: Deterministic timeout harness proves resilient local state resumption independent of environmental variance. |
| **G. QA / Validation / Debugging Architecture** | 10.0 | 10.0 | 0 | Unchanged (Max). |
| **H. Governance & Change Control** | 8.0 | 8.0 | 0 | Unchanged (Max). |
| **I. Scope Expansion via AI** | 5.0 | 5.0 | 0 | Unchanged (Max). |

**Track 2 Total:** **99.50 / 100**

---

## 4. Traceability & Comparability Verdict

*   **Track 1 Comparability:** VALID. Evaluated against the exact 10 dimensions/scale of the HF03/T01 frozen state.
*   **Track 2 Comparability:** PARTIAL / ADMISSIBLE WITH CONDITIONS. Evaluated against the V3 rubric. Framework engineering was deemed admissible as applied-AI capability evidence for dimensions A-D and F, supplementing rather than replacing original product audits.
*   **Evaluator Identity/Date:** GEMINI/MAGAI, September 28, 2026.
*   **Input Binding:** `VPL_DUAL_SCORE_FINAL_EVIDENCE_R01.zip` (SHA-256: 7bd8eacbc506414292e80849521317184b219eb1667530f6ef861c52dbca6512).
*   **Current Action:** Report Correction Only (R02). No tests rerun, no public HTML touched, no scores altered from R01.
CONTROL — Independent Critique & Continuous Improvement

CONTROL is a cornerstone of the VPL operating system.

Peter DeCaro's Lean Six Sigma Black Belt discipline is translated directly into the VPL Build Framework. CONTROL is the mechanism that keeps an improvement from decaying after a successful build or assessment cycle. The framework is repeatedly graded against independent critique, deterministic QA and recognized industry standards, then updated only when findings are validated and generalize beyond a single product.

DEFINE the architecture question. The capability or system property under review is stated before evidence is scored. Comparison population, included dimensions, exclusions, authority, non-goals and evidence ceiling are frozen so the test measures the intended construct.
MEASURE against preserved evidence. Exact repository commits, manifests, QA outputs, state/recovery evidence, architecture maps and evaluator inputs are preserved. Claims are tied to observable artifacts rather than recollection or polished narrative.
ANALYZE through independent critique. Independent models and technical review surfaces challenge the evidence, architecture decisions, boundaries, gaps and assumptions. Disagreement is retained long enough to identify whether it reflects a real design weakness, an evidence gap or reviewer overreach.
IMPROVE with bounded remediation. Validated findings are converted into small, testable changes with source boundaries, acceptance criteria, recovery rules and evidence requirements. The objective is stronger architecture, not a cosmetically higher score.
CONTROL the improved state. The corrected condition is frozen, re-tested and banked. Regression checks, source identities, change-control records and repeatable operating rules prevent the system from silently returning to an earlier state.
REPEAT as a learning system. Validated lessons are backfilled into the VPL Build Framework only when they generalize beyond one product. The framework therefore compounds learning across products instead of treating every build as an isolated experiment.

Independent criticism is a CONTROL input — and a business operating principle.

VPL treats independent criticism as structured process data. Specialized review agents, external model families and deterministic checks are deployed against the framework to look for drift, weak boundaries, stale assumptions, evidence gaps and opportunities to simplify or strengthen control. Valid findings are documented, prioritized, remediated, re-tested and banked. When the lesson is reusable, it is backfilled into the VPL Build Framework so the operating system becomes stronger with each product and each review cycle. This Define → Measure → Analyze → Improve → Control loop is a continual-process-improvement standard, not a one-time certification exercise.

Future evaluator access requirement — adopted after the Claude Opus 5 run.
Every future frozen assessment package must provide full folder access with all source and evidence contents already unzipped/extracted, while retaining the frozen ZIP/archive for integrity and reproducibility. Evaluators must not depend on archive-extraction capability to inspect source, QA, runtime, security or negative evidence. Each evidence group must include at least one directly readable non-archive artifact; any transformation from frozen ZIP to extracted tree must preserve paths and record source hash, extracted-tree hash, transformation steps and equivalence checks. A platform dry run must confirm readable source, HTML/runtime evidence, QA artifacts and manifests before scoring begins.
The Master Control

The assessment is not a one-time credential. It is the laboratory's recurring control system.

The purpose is continuous humility: submit current work to the marketplace of models, tools and recognized standards; compare it with the frozen baseline; identify drift or new best practice; and feed validated improvements back into the VPL Build Framework. This is the direct descendant of Peter's operating career: WBR/QBR cadence, KPI control, leading/lagging indicators, root-cause analysis, standardized handoffs and closed-loop corrective action—translated into an AI architecture laboratory.

The Hyundai analogy — operating inspiration, not a scoring authority. Peter uses Hyundai as a practical metaphor for the control philosophy: quality has to be proven through repeated testing, disciplined production controls, measurable reliability, and a willingness to stand behind the product. Hyundai publicly describes hundreds of internal and road quality tests and backs new vehicles with a 10-year/100,000-mile powertrain limited warranty. VPL does not borrow Hyundai as a software standard; it borrows the operating lesson: confidence should be earned by controls strong enough to expose failure and support accountability. Hyundai quality process ↗ Warranty evidence ↗
DMAIC Governance · Control Is the Product

Defined control cadence · monthly scans, quarterly scoring

The defined master-control cadence uses monthly scans for drift, open findings and relevant external changes, with a separately frozen full assessment each quarter. These are operating requirements, not evidence that a scheduled task is installed or that every planned cycle has run.

DEFINEFor a monthly scan, lock the change questions and evidence boundaries. For a quarterly assessment, additionally freeze the specimen set, scoring instrument, evaluator panel and acceptance criteria.
MEASUREInspect a bounded monthly sample for changes and regression. Score only in the separately authorized quarterly cycle, using its frozen instrument and machine-validation evidence.
ANALYZECompare current results to prior baselines, evaluator disagreement, authority gaps, model/standards changes and recurring architectural weaknesses.
IMPROVEConvert validated findings into bounded VPL Build Framework revisions, project controls, templates, modules, prompts or engineering practices.
CONTROLVersion the changes, preserve previous scores, rerun targeted checks, monitor adoption and carry unresolved gaps into the next controlled cycle.

Control-cycle inputs

  • Representative codebase/project sample: scope must be locked for each authorized cycle
  • New or materially changed AI model capabilities and repository tooling.
  • Changes to NIST, ISO/IEC/IEEE, SEI, OWASP, MITRE, CSA, OpenSSF/SLSA and other approved authorities.
  • New VPL Build Framework modules, controls or architecture decisions.
  • Open prior-month weaknesses, disagreements and improvement actions.

Required outputs · monthly scan / quarterly assessment

  • Frozen run manifest and evidence hashes.
  • Monthly finding/disposition log; quarterly score matrix and disagreement report for the authorized evaluator panel.
  • Authority-alignment delta report.
  • Machine-validation delta report.
  • Quarterly architecture comparison and human-agency finding under the frozen construct; no new score inferred from monthly monitoring.
  • Framework backfill recommendations classified MUST / SHOULD / OPTIONAL / DO NOT ADD.
  • Closed-loop control log showing what changed, why and whether it improved the next run.
Marketplace / StandardsModels, authorities, tools, current best practice
Frozen Quarterly SampleEvidence groups frozen for that authorized cycle
Independent EvaluationDeclared evaluator panel + authority + machine evidence
Gap / Delta AnalysisRegression, improvement, new practice, disagreement
VPL Framework BackfillModules, controls, prompts, architecture standards
Next Build CycleAdopt, validate, monitor, resubmit
HISTORICAL BASELINE SETNegative-finding closure rate will be measured from this completed cycle forward
HISTORICAL ARCHITECTURE BASELINEFirst fully frozen independent architecture benchmark established for longitudinal control, repeatable comparison and future regression detection.
EARLIER FIVE-MODEL CYCLEEvaluator/standards changes assessed in the completed architecture cycle
EARLIER REMEDIATION CONTROLSFramework remediation controls created because evidence demanded change
Control principle: the assessment must be willing to lower the score, expose regressions, invalidate obsolete practices, and force framework changes when external evidence changes. A control system that only confirms improvement is not a control system.
Ecosystem Refresh Protocol

The rubric stays stable; the evidence of best practice is continuously refreshed.

Monthly control scans do not change the scoring rules to chase trends; full scored assessment is quarterly. The architecture construct remains versioned and controlled. What changes is the external evidence base: standards revisions, new threats, new model capabilities, new development patterns and stronger validation methods.

Refresh DomainControl QuestionControl ActionMetric Placeholder
Standards & AuthoritiesDid any approved authority publish or materially revise guidance relevant to the rubric?Review change; map affected controls; decide MUST/SHOULD/OPTIONAL/NO CHANGE.[# CHANGES REVIEWED]
Model Architecture & CapabilityDid any panel model add materially relevant reasoning, coding, tool, repo, context, agent or governance capabilities?Document change; determine whether evaluation surface or architectural best practice should change.[# MODEL CHANGES]
Security / Threat LandscapeDid OWASP, MITRE, NIST, CSA or tool evidence identify new material AI/application threats?Update threat/control crosswalk; test applicable projects.[# NEW MATERIAL RISKS]
Architecture PracticeDid new industry/academic evidence materially alter accepted architecture or AI-system practice?Independent review; do not adopt novelty without evidence.[# PRACTICES ASSESSED]
VPL Internal LearningWhat repeated project failures or improvements indicate a framework weakness or strength?Backfill framework, then verify in next controlled sample.[# FRAMEWORK CHANGES]
Outcome ValidationDid control-driven changes improve later evidence rather than merely add process?Compare next-run scores, failures, cycle time, cost and recurring defect patterns.[CONTROL EFFECTIVENESS]
How To Read The Final Report

The intended conclusion is narrower—and stronger—than “AI made good software.”

The evidence must support whether persistent human architectural direction materially shapes the coherence, governance, progression and business usefulness of AI-assisted outcomes.

The test does not claim that AI could not have generated individual artifacts. It asks whether recurring architectural fingerprints, controlled decisions and longitudinal learning across independent systems are better explained by an accountable human architect than by ungoverned model capability alone.

The master control keeps the lab honest over time.

The defined quarterly control calls for a newly authorized frozen evidence cycle, independent judgment, an identified authority snapshot and objective validation. This page does not assert that a recurring task is active or that a future cycle has executed. The framework is expected to change when the evidence says it should.

Historical common evaluator contract · not the informed-successor protocol
Evaluator Delivery Contract

Every model receives the same scorecard, not a model-specific interpretation.

The public scorecard and the evaluator instrument are intentionally coupled. Before formal freeze, the measurement system itself is subjected to five de novo frontier-model methodology reviews. Claude, Grok, Gemini, DeepSeek and OpenAI each completed a methodology-only review before Candidate A scoring. Their accepted MUST controls were reconciled into v1.5; disagreements were preserved and dispositioned rather than resolved by simple model vote. After all five reviews are dispositioned and the method/evidence/model configuration is frozen, each primary evaluator receives the same frozen constructs, evidence-access rules, scoring tables, authority snapshot, negative-finding obligations and output schema.

Pre-freeze rule: Methodology review is separate from Candidate A scoring. All five methodology reviews were completed before Candidate A scoring; raw reviewer conclusions are treated as methodology-review evidence and the operative instrument for that historical cycle was v1.5. After freeze, model-specific rubric changes are prohibited: evaluators may disagree with the instrument, but all official runs must score against the same frozen method.
Required evaluator outputWhy it is mandatoryStatus before freeze
Evidence-access declarationSeparates true evaluator disagreement from incomplete repository access.LOCK AT FREEZE
100-point architecture scorecardProvides common dimension weights and evidence expectations across all five models.LOCK AT FREEZE
Human Architectural Agency report cardTests the human contribution rather than rewarding AI-generated implementation volume.LOCK AT FREEZE
Authority-alignment reportCross-checks evidence against recognized architecture, AI-risk, security and provenance practices without implying certification.LOCK AT FREEZE
Negative / counter-evidence registerMakes weaknesses, regressions, ambiguity and disconfirming evidence first-class control inputs.LOCK AT FREEZE
Improvement prioritiesTransforms criticism into a controlled Build Framework backlog for the next controlled cycle.LOCK AT FREEZE
Confidence + disagreement notesPrevents false precision and preserves meaningful divergence among independent evaluators.LOCK AT FREEZE
V2 remediation requirement: the next evaluator package must expose one sole operative scoring authority. Legacy evaluator instructions that conflict with Evaluation Standard v1.5 on project scope, dimensions, weights, per-dimension scale, HAA or output naming must be removed from the evaluator entry path or explicitly marked superseded before freeze. The scorecard itself should sit outside the blind evaluator evidence package to reduce anchoring exposure.
Independent Review Registry

Primary sources a skeptical reviewer can inspect without trusting this page.

This report is designed to be challenged. The links below go to the issuing organizations or canonical repositories used to define the external evidence layer.

NIST
NIST AI Risk Management Framework

Released 2023; consensus-driven voluntary framework for trustworthy and responsible AI, with a 2024 Generative AI Profile.

nist.gov/itl/ai-risk-management-framework ↗
ISO
ISO standards development + ISO/IEC 42001

ISO describes its process as expert, multi-stakeholder and consensus-based; 42001 is the AI management-system standard.

ISO standards process ↗ · ISO 42001 ↗
SEI
CMU SEI Architecture Tradeoff Analysis Method

Formal architecture-evaluation method with reports dating to the late 1990s and continuing SEI guidance.

SEI ATAM ↗
OWASP
OWASP GenAI Security Project

Open global security community; current GenAI LLM Top 10 guidance and active source repository.

OWASP GenAI ↗
MITRE
MITRE ATLAS

Adversarial threat knowledge base for AI-enabled systems from a not-for-profit operator of U.S. federally funded R&D centers.

atlas.mitre.org ↗
CSA
Cloud Security Alliance AI Controls Matrix v1.1

2026 vendor-neutral framework with 247 AI control objectives across 18 domains and mappings to major standards.

CSA AICM ↗
OSSF
OpenSSF Scorecard

Canonical open-source repository for machine-verifiable security and repository-health checks.

github.com/ossf/scorecard ↗
SLSA
SLSA specification

Software artifact provenance and build-integrity framework used as a bounded supply-chain evidence source.

slsa.dev/spec ↗
MySmartRouter V5.1 · preserved staging evidence

MySmartRouter turned VPL's build framework into a tighter product operating system.

The preserved MySmartRouter V5.1 staging cycle converted practical build pain into reusable framework controls: explicit feature freeze, grouped testing, provider capability matrices, context-driven history, persistent navigation, source-to-package proof, atomic rollback and anti-drift reminders.

What changed: The framework now distinguishes feature definition from stabilization, separates General Settings from section settings, treats UX consistency as a release requirement, requires provider integrations to be reachable end-to-end, and makes exact source/package correspondence part of release evidence.
SmartRouteMulti-model/provider decisioning with explicit model authority and telemetry.
OpenRouterMulti-model PAYG route and model inventory source.
API MartGoverned provider-route target requiring explicit configuration and capability mapping.
TelnyxPAYG multi-model inference route candidate.
RunwareUnified PAYG inference/media provider candidate.
ReplicatePAYG image/video model execution candidate.
RunwayImage/video generation API candidate.
Stability AIImage-generation API candidate.
TelegramExternal governed notification relay.
PushoverMobile push notification transport.
HostingerHosting and optional product scheduling; V5 persistence truth remains invocation-bound.
LovableDownstream design/SaaS execution with auditable return deltas.
GitHub ActionsExact source identity, CI checks and evidence-backed packaging.
Applications & Technologies

Technology surfaces used across the VPL portfolio.

This inventory is consolidated from the HTML case studies in the Vantage Product Labs website folder. It shows the applications, platforms, model providers, engineering tools, data services, browser/desktop runtimes and operating systems represented in the portfolio—not a claim that every product uses every tool.

The pattern is deliberately mixed: frontier models for reasoning and evaluation; AI-assisted development environments for implementation; browser, desktop and server technologies for product delivery; automation and acquisition services for workflow/data movement; and source-control, hosting and operating tools for governed execution. Recent assessment and remediation work expands that surface with Magai, Mistral, Kimi, GLM/Zhipu AI, Cursor Cloud Agents and GitHub Actions-based hosted CI.

Current core stack

Primary tools in the current VPL operating architecture.

Embedded marks · usage varies by product and build stage
OpenAI / GPTReasoning & execution
ClaudeArchitecture & review
GeminiIndependent evaluation
CursorRepository execution
GitHubSource & CI
Google DriveGovernance & evidence
HostingerHosting & runtime
AI Models & Evaluation
OpenAI / GPTReasoning, generation, multimodal analysis and evaluator workflows.
Claude / AnthropicArchitecture, coding, review and long-context development; Git source and Google Drive control/evidence connections.
Gemini / GoogleMultimodal reasoning and Google-connected workflows.
Grok / xAIIndependent reasoning, research and comparative model evaluation.
DeepSeekTechnical reasoning and multi-model comparison.
MistralIndependent model-family evaluation and comparative architecture review.
Kimi / Moonshot AIIndependent long-context evaluation and model-family comparison.
GLM / Zhipu AIIndependent architecture capability evaluation and comparative reasoning.
AI Development, Routing & Prototyping
MagaiMulti-model workspace used for controlled evaluator sessions, including Gemini-family assessment workflows.
Cursor Cloud AgentsRepository-bound AI implementation, bounded repair, PR return and cloud execution workflows.
GitHub ActionsHosted CI, exact-commit validation, static checks and unattended regression workflows.
CursorAI-assisted codebase implementation and repository work; Git plus Google Drive with actor-specific access checks.
OpenRouterUnified model gateway, routing, fallbacks and usage visibility.
LovableRapid product/UI prototyping and governed handoff workflows.
BoltRapid AI-enabled application prototyping.
Automation, Research & Data Acquisition
MakeVisual workflow automation and systems integration.
n8nWorkflow orchestration, API automation and agentic processes.
ZapierSaaS automation and event-driven integrations.
ApifyModular scraping and web-data acquisition.
RapidAPIExternal API marketplace/provider integration.
Bright DataCommercial web-data infrastructure.
OxylabsProxy and web-intelligence infrastructure.
Application Engineering & Delivery
PythonApplication logic, automation and data processing.
StreamlitInteractive Python application interfaces.
PHPServer-side web applications, APIs and hosted workflows.
MySQL / MariaDBRelational persistence, analytics and operational state.
HTML5 / CSS / JavaScriptStandalone responsive interfaces and browser interaction.
Git / GitHubSource control, repositories, versioning and deployment governance.
HostingerProduction hosting, databases, product cron and deployment through both API/MCP and governed Git routes.
Browser, Desktop & Native Surfaces
Google Chrome / ChromiumPrimary browser runtime and mobile/desktop web target.
Chrome Extensions / Manifest V3Extension service workers, permissions and controlled page interaction.
TauriLightweight desktop shell for MyIdeaMax Windows evolution.
RustNative command layer behind Tauri desktop capabilities.
Windows Native IntegrationGlobal shortcuts, clipboard, process launch and system tray workflows.
AutoHotkey v2Optional replaceable Windows hotkey/automation adapter.
ShareXOptional capture adapter for screen-region/window workflows.
Workspace, ATS, Communications & Operating Tools
Google Workspace / DriveDocs, Sheets, Drive, build artifacts and project continuity.
TrelloBoard-based workflow and build-task management.
GetResponseEmail marketing and campaign delivery; V4 defines consent-aware lifecycle integration separately from paid entitlement.
WorkdayATS certification target in ResumeRocket.
GreenhouseGoverned ATS certification target in ResumeRocket.
LeverGoverned ATS adapter/certification target.
TwilioTwo-way mobile transport for build-governance alerts.
PushoverApp-based mobile operational alerts.
TelegramBot-based notifications and operator status updates.
V5 verification and assessment · September 28 evidence
Python · unittest & subprocessVerification scripts, focused tests and controlled child-process timeout/recovery checks. Python 3.11 reviewer runs; 3.13 primary runs are separately attributed.
JSON Schema · Python jsonschemaStructured evidence validation in the recorded Action 3 environment. A validator is an evidence tool, not proof of complete product behavior.
LinuxThe recorded independent replay environment. Windows execution was not established by these review packets.
MagaiDelivery environment for Gemini’s informed successor assessment. It is not a separate additional evaluator or a provider identity attestation.
Historical V5.1 platform additions · source/contract scope
API MartGoverned SmartRoute provider-route target.
TelnyxPAYG multi-model inference candidate.
RunwarePAYG multimodal/text inference provider candidate.
ReplicatePAYG image/video model execution.
RunwayImage/video generation API.
Stability AIImage-generation API.
HostingerHosting, staging, production and product cron; API/Git route selection, live validation and rollback.
LovableDownstream design execution with governed return path.

All marks in this section are embedded directly in the HTML as inline SVG/HTML identification marks or embedded official logo images. No external logo paths are required for rendering. Portfolio usage varies by product and build stage; project-specific case studies remain the source of truth for implementation details.

Source, security and integration roles evidenced in the newer work

sqlite logoSQLite · signer state

Security and persistence

Source-defined private replay/challenge ledger used by the MySmartRouter runtime signer. Its presence in source and CI does not establish an installed signer or persistent CURRENT state.

mariadb logoMariaDB · PHP/PDO integration tests

Database verification

Database service exercised by the isolated exact-head MySmartRouter CI record. Existing MySQL architecture remains relevant; the MariaDB test environment is identified separately from a production database.

libsodium logoPHP sodium / libsodium · Ed25519

Signature verification

PHP sodium functions provide the source-defined signature verification used by the signer/verifier path. This describes the extension and primitive, not a security certification or a newly installed service.

openssl logoPHP OpenSSL · AES-256-GCM

Private configuration

The inspected Google-authentication and connection-service source uses OpenSSL encryption for private token/configuration material. No tokens are published and no new production key or connection is created by this documentation update.

curl logoPHP cURL / libcurl

Provider and Google transport

Source-level HTTP transport for provider calls and Google services. A configured route or callable transport is not evidence that every provider or account is authenticated and working.

github actions logoGitHub Actions · PHP / MariaDB CI

Source-controlled verification

The September 25 R69 record binds candidate source to isolated CI: 291 baseline blobs preserved plus 52 additions, with failing-first evidence and corrected pilot tests. Independent exact-head acceptance and installed/runtime verification remain separate.

google drive logoGoogle Drive · OAuth 2.0 / PKCE

Evidence and Google integration

Versioned review packets and authority handoffs use Drive. MySmartRouter source also contains the Google authorization/PKCE boundary. Public website evidence uses same-site resources, never a visitor’s access to private Drive.

nodejs logoTelegram · external Node.js relay boundary

Notifications and integration

The documented relay lies outside the inspected PHP package. Its Node.js runtime is an integration boundary, not a claim that the relay was installed, tested or activated by the V5 review.

meta-llama logoMeta / Llama · provider catalog

Configured routing

Provider artwork and catalog references are retained as source-configured routing context. Catalog presence is not proof of a live, authenticated model execution.

qwen logoQwen · provider catalog

Configured routing

Source catalog/provider references identify this model family without claiming a new direct connection, live route or independent assessment.

Named roles are source-, contract- or test-evidenced as stated—not blanket claims of installed production services. JSON, Markdown, YAML, CSV, ZIP and SHA-256 support interchange, test workflows, provenance and byte identity; they are formats or mechanisms, not additional vendor endorsements.

V4 expansion · membership, payments & scheduled orchestration

From a collection of tools to reusable operating contracts.

V4 connects the existing development stack to a governed customer lifecycle: lead capture in GetResponse, payment through Stripe, account and entitlement control in aMember, and product access backed by explicit evidence. These are reusable framework contracts; implementation and activation are verified separately for each product.

aMember

Reusable account, membership and protected-access integration. V4 defines entitlement checks, payment-to-membership mapping, safe return-to-app behavior and recovery tests.

Stripe

Payment and checkout integration with verified server-side outcomes, existing product/price reuse, authenticated events and duplicate-safe fulfillment.

ChatGPT Scheduled Tasks

Capability-gated scheduled orchestration and read-only readiness pilots. Current authority is reloaded on each invocation; defined roles, created tasks and verified runtime remain distinct.

The aMember and Stripe marks are embedded from their official websites; the scheduled-task card reuses this portfolio’s existing OpenAI identification mark. No new external logo dependency is required. Technology inclusion identifies its role in the portfolio or framework, not a claim that every service is live in every application.

Case Studies

Architecture demonstrated through working product systems.

The strongest assessment findings are anchored in working systems, not abstract claims. MySmartRouter and MyFlightWatcher provide two materially different examples of Peter's refined architecture discipline: multi-model AI control, source authority, state/recovery design, integration boundaries, deterministic QA, governance and evidence-driven remediation.

Flagship architecture · historical product baseline / paused V6 candidate

MySmartRouter.com

One operator-controlled workspace above the model layer—built to keep AI-assisted work organized, governed, resumable and cost-aware.

Problem

Serious AI work becomes fragmented across models, providers, projects and conversations. That creates repeated context rebuilding, duplicated work, uncertain provider/model selection, disconnected histories and weak visibility into the cost and provenance of each request.

Architecture Response

The preserved browser-based multi-pane architecture separates requested model choice, routing, conversation state, cross-chat handoff, Google Drive authority, provenance and usage/cost visibility. The newer V6 Pilot 01 work focuses on admitting source-bound evidence rather than trusting a signed PASS alone; it does not turn every catalog entry or specified integration into a live service.

Architecture Signals

  • Provider/model routing with requested-versus-served identity; catalog presence is not proof of live execution.
  • Independent pane state, governed context and traceable attachments in the preserved product design.
  • PHP signer/verifier boundaries using sodium/libsodium signatures and a SQLite replay/challenge ledger.
  • Google OAuth/PKCE, OpenSSL-protected configuration and cURL transport are source-defined integration boundaries.
  • The dated R69 CI record preserved 291 baseline blobs plus 52 additions and recorded 16/16 pilot cases; exact-head independent acceptance and installed runtime CURRENT remained separate.
sqlite logoSQLiteMariaDBlibsodium logolibsodiumopenssl logoOpenSSLcurl logocURLgoogle drive logoGoogle DriveGitHub Actions
Evidence and status: September 25 R69 source/CI record; accepted R59 baseline retained. MySmartRouter remains paused. Read the product architecture and dated evidence. The embedded assessed-source selection on this page covers V5 verification tools, not a newly published MySmartRouter product tree.
Corroborating Deployed Case · Airfare Intelligence

MyFlightWatcher

A multi-provider airfare monitoring system that replaces repetitive manual fare checking with governed acquisition, normalized history, analytics and actionable alerts.

Problem

A traveler may know the route, dates, airports, nonstop preference and target price, yet still have to repeatedly search multiple sources to understand whether the market moved, which provider is lowest and whether the fare is actionable.

Architecture Response

MyFlightWatcher separates trip configuration, scheduling, provider acquisition, normalization, MySQL operational storage, analytics/alerts and raw-data archival. Provider-specific cadence, quota protection, retry behavior and health visibility are governed independently while downstream analytics work from a common fare representation.

Architecture Signals

  • Provider-adapter architecture with normalization into a common fare model.
  • Provider-specific scheduling, request accounting, quota and retry governance.
  • Historical fare intelligence including lows, averages, trends and first-observed timing.
  • Target-fare alerting through governed notification logic.
  • Separation of compact operational facts from large raw provider payload archives.
Source guide: Vantage Product Labs website · myflightwatcher-v4.html and the website case-study index.
About Peter

Operator → Architect → AI-Native Builder

Peter DeCaro

Peter DeCaro is an operations and technology-focused product builder with more than 25 years of experience improving, automating and scaling complex business operations. Across his career, he has operated at the intersection of customer operations, revenue operations, process improvement, technology implementation and organizational scale. His experience includes leadership and transformation work associated with organizations including Fluent, IAC Applications, AOL and KIT Digital, as well as consulting and product-development work through Vantage Solutions Group and Vantage Product Labs.

His career has consistently centered on a practical question: how can technology remove operational friction, create repeatable decision systems and allow people to produce better outcomes with less manual work? Long before generative AI became a mainstream operating tool, that work included process redesign, workflow automation, KPI governance, CRM and ERP implementation, customer-success operating models, vendor and workforce management, executive reporting and the stabilization and scaling of growing businesses.

Peter has overseen significant revenue operations, built programs supporting sizeable customer-success and service organizations, and led improvement initiatives across high-volume, technology-enabled environments. He is Six Sigma / Lean Six Sigma trained and has spent much of his career applying continuous-improvement principles in live operating environments. He also brings project-management training and certification, familiarity with Agile/Scrum operating concepts and a modular approach to process architecture.

Why that background matters now

AI has compressed the distance between architecture and execution. Peter's long-standing strengths—decomposing systems, clarifying requirements, organizing specialists, measuring performance, controlling change and improving workflows—map directly onto modern AI-assisted product development. Models can now perform portions of engineering, research, analysis, copy, QA and design work; the architect's role becomes deciding what should be built, how the pieces connect, which capability should perform each task, how quality is verified and how the system improves.

That is the operating model visible across MySmartRouter, MyFlightWatcher, ResumeRocket, MyRocket Studio / RocketCore, MyRocketBuilder, IdeaMax and TrueReply. The portfolio demonstrates an increasingly mature form of governed AI leverage: Peter remains accountable for product intent and architecture while AI systems are used as specialized development, reasoning and review resources.

From operational architecture to product architecture

The transition is evolutionary rather than abrupt. SaaS environments, application-enabled operations and growth-stage technology companies provided decades of exposure to how software changes workflows and how workflows must be designed to scale. Current AI-native work extends the same principles into a new medium: modular applications, multi-model systems, persistent state, APIs, automated data acquisition, structured evaluation and reusable product engines.

Peter's value is therefore best understood not as a claim to be the deepest specialist in every technical discipline represented in the stack. It is the ability to operate effectively across those disciplines, learn rapidly, recognize dependencies and failure modes, and organize AI-assisted execution around a coherent business and product outcome.

The result is a distinctive profile: an experienced operations architect using AI to dramatically expand the range, speed and complexity of products one person can design, govern and bring into working form.
Professional Development / AI / Continuous Improvement

Certifications

Peter's certifications reflect the two disciplines that converge in MyRocket Studio: formal continuous-improvement methodology and hands-on development of AI-enabled operating systems. The combination supports an operator-builder approach in which automation, process control, prompt engineering, AI agents and production application design are treated as connected capabilities rather than isolated technologies.

Professional Development & CertificationsContinuous Learning
Six Sigma / Lean Process Excellence
Six Sigma Black BeltContinuous Improvement / Process Excellence
Six Sigma Green BeltContinuous Improvement / Process Excellence
Six Sigma Yellow BeltContinuous Improvement / Process Excellence
Lean Six SigmaLean + Six Sigma Process Improvement
AI, Prompt Engineering, Agents & Application Development
Prompt Engineering CertificationQuantum Leap Academy
No-Code AI Prompting: Websites and ApplicationsUdemy
OpenAI Codex Full Course 2026: AI Coding, Automation, AgentsUdemy
OpenAI Codex Masterclass: Build Your AI Operating SystemUdemy
Advanced Master AI Prompt EngineeringUdemy
ChatGPT for Customer SupportGreat Learning
Building AI Voice Agents for ProductionDeepLearning.AI
ChatGPT Prompt Engineering for DevelopersDeepLearning.AI
Academy Accreditation - AI Agent FundamentalsDatabricks Academy
Generative AI FundamentalsDatabricks Academy
Historical professional aptitude and role-fit report · separate fourteen-dimension instrument
Direct Architect Evaluation & Role-Fit Assessment

Independent Architect Aptitude & Professional Capability Report Card

Historical professional-aptitude assessment: the original report recorded a raw 93.90, publicly displayed as 94.0, across fourteen aptitude dimensions. This is a separate historical instrument, not either of the September 28 capability scores. Its original findings and classification are preserved below.

2026 Google Gemini 3.8 Flash · Independent Architect Aptitude Audit
94.0/100
Principal-Level Architect Capability
Gemini archetype: Operations Systems & AI Governance Architect · Confidence: High
What this score measures: Peter DeCaro's professional aptitude as the architect—systems thinking, judgment, operational translation, human direction of AI, orchestration, recovery thinking, governance, technical communication and process control.
#Professional AptitudeWeightRating /10Weighted ScoreClassificationConfidence
1Process-Control Mindset59.74.85Principal-LevelHigh
2Human Direction of AI89.67.68Principal-LevelHigh
3Governance / Change Control79.66.72Principal-LevelHigh
4Operational Translation89.57.60Principal-LevelHigh
5Agent Orchestration & AI Leverage89.57.60Principal-LevelHigh
6Workflow / State / Recovery Thinking79.56.65Principal-LevelHigh
7Technical Communication49.53.80Principal-LevelHigh
8Systems Thinking109.49.40Principal-LevelHigh
9Problem Decomposition99.38.37Principal-LevelHigh
10QA / Debugging Discipline79.36.51Principal-LevelHigh
11Architectural Judgment109.29.20ExpertHigh
12Learning Agility & Adaptation59.24.60ExpertHigh
13Integration Reasoning79.16.37ExpertHigh
14Inventive Problem Solving59.14.55ExpertHigh
9.7 Process-Control Mindset 9.6 Human Direction of AI 9.6 Governance / Change Control 9.5 Operational Translation 9.5 Agent Orchestration & AI Leverage 9.5 Workflow / State / Recovery 9.5 Technical Communication 9.4 Systems Thinking 9.3 Problem Decomposition 9.3 QA / Debugging Discipline 9.2 Architectural Judgment 9.2 Learning Agility 9.1 Integration Reasoning 9.1 Inventive Problem Solving

What Gemini says the evidence demonstrates

Operations Systems & AI Governance ArchitectGemini's independent architect archetype for Peter DeCaro.
Exceptional Fit — Principal Applied-AI ArchitectGemini's role-fit conclusion for the principal applied-AI architecture role.
Human authority above AI executionStrong evidence that objectives, constraints, acceptance, remediation and final architectural disposition remain human-controlled.
AI orchestration as an engineering systemModels and agents are separated into director, execution, review and deterministic verification roles rather than treated as one undifferentiated assistant.
Operations discipline translated into architectureGemini specifically recognized the application of Lean/Six Sigma and operational-control thinking to AI-enabled software delivery.
Commercial and technical reasoning combinedEvidence showed technical choices tied to operating constraints, rate quotas, unit economics, workflow reliability and real-world product behavior.
Gemini's evidence-grounded professional positioning: Principal Applied-AI & Systems Architect combining operations engineering, Lean Six Sigma discipline, deterministic governance, multi-agent orchestration, resilient stateful architecture and uncompromised human control.
Historical comparison only. The fourteen-dimension professional-aptitude result and the original V3 capability result were different instruments. Today’s accepted 95.00 systems-architecture track and 99.50 informed applied-AI track are explained in the current assessment sections above. No historical aptitude result has been recalculated.

Historical evaluator context · not a score guarantee

Google's Gemini family is one of the major frontier-model families used for software engineering, repository-scale analysis, multimodal reasoning and long-context work. Google's public Gemini 2.5 Pro materials describe a 1-million-token context window, the ability to work across very large information sets including entire code repositories, and strong public coding performance. Gemini Code Assist also supports whole-workspace context and repository-scale development workflows.

In the historical report, model context and coding tools explained the choice of evaluation environment; they were not evidence of correctness. The original blinded-method claims apply only to the historical protocol. The September 28 successor was informed/non-blind and must be judged from its bound source, retained limitations and corrected rationale, not the Gemini family name.

Important attribution: the preserved audit identifies the evaluator as Gemini 3.8 Flash via Magai. Google's public benchmark and context-window materials cited here describe the public Gemini family (including Gemini 2.5 Pro / Code Assist), not a public benchmark claim for the specific Magai interface label used in this audit.

Continued development · operator discipline in an AI-native environment

Continuous improvement becomes an engineering contract.

The V4 foundation and reviewed V5 continuation extend Peter’s operating background into explicit delivery discipline: decide where specialist effort adds value, preserve the accepted baseline, test the actual integration, invite independent challenge and bank a recoverable result before the next dependent wave. The growth is visible in the framework’s controls and reusable templates—not in a retroactive change to earlier professional assessments.

Portfolio / architecture capability update · 24 October 2026 · for external evaluation

Recent delivery progress: from AI architecture controls to a governed product portfolio

This update supplements the accepted September 2026 professional/architecture evidence without revising those scores, biographies or evaluator attributions. New evidence is provided as a dated current-state summary, not as a claim that an independent October reassessment has been completed.

Broadening operational useVPL's build framework now spans portfolio websites, deployable desktop/HTML tools, multi-provider AI runtimes, research/idea tools, resume optimization, recipe companion software and template-driven publishing.
Increasing build disciplineSRV6 Light and Manual modes, exact version progression, UAT, evidence preservation, actor-specific review and accountable owner approval convert speed into repeatable controlled execution.
Stronger separation of proofRelease identity, test results, staging/deployment, public portfolio badge and verified production runtime are different facts. Product-specific recaps determine the actual current release state.

Demonstrable recent portfolio advances

New case studies: MyCrockPot and MyScreenScraper are documented with source-based capabilities, matching infographics and integrated portfolio previews. Web delivery: the optimized index.html is 17.4% smaller than the preserved index_final.html while retaining the page's cards, links and status structure. Measured user-device speed remains a follow-up test.

Product architectures: MyTurboGPT R21 focuses on controlled provider/model selection, artifact placement and history; MyVibeSuite V2.0 B011 on constrained review-and-edit experiences; MySmartRouter V7.1 B021 on UAT/coordination; ResumeRocketPro V7.2E diagnostic evidence on role matching, ATS terms and preserved source claims; MyIdeaMax on repeatable research UX; MyScreenScraper V2.0 on capture, markup, recording, history and deployment packaging.

Current portfolio maturity and limitations

Per the published homepage, GuitarFakeBook, MyFlightWatcher, MyJamTracks, MyOfferCountdown, MyCrockPot, TriadSoloLab, MyVoiceVibe, InFlow and TrueReply carry in production badges. MySmartRouter remains the lead flagship while its card says in dev. B026. ResumeRocketPro, RocketFill, MyScreenScraper, MyRocketStudio, KDP AI Secrets, MyIdeaMax and MyVibeSuite retain development labels. A badge is not itself independent operational validation.

What Claude should be able to evaluate later

The work presents a testable claim about architectural sophistication: VPL is evolving from reusable prompts and prototypes toward saved-state governance, bounded AI work allocation, independent verification, controlled release paths and evidence-backed multi-product operations. Claude should evaluate that claim against the operative SRV6 authority and original project ledgers rather than accepting portfolio presentation at face value.

Assessment boundary: Historical 94.50 and 97.00 scores remain attached to their original methods and dates; this October progress is supplementary evidence only. The revised VPL_BUILD_FRAMEWORK_CONSOLIDATED_REFERENCE_V2.html provides the full annotated status register, source links and review questions. Claude feedback will be backfilled only after review.