# Answer

The AI Flight Recorder home lab operates under a dual mandate: maintain rigorous, transparent internal telemetry for model reliability while enabling safe, standardized public reporting that respects privacy boundaries and synthetic data governance. This policy establishes a comprehensive framework for benchmark artifact management, prompt handling, failure tracking, metric standardization, and public disclosure. The policy is engineered to balance open scientific communication with strict home-lab security, ensuring that all published materials are sanitized, reproducible, and compliant with the synthetic governance rules provided. 

At its core, the policy dictates that benchmark artifacts follow a structured lifecycle: general artifacts are retained for a baseline period, while raw private prompts are preserved locally until explicit deletion to support post-hoc debugging and reliability analysis [S1][S6]. Public-facing materials, including screenshots and comparison reports, must adhere to strict metadata requirements and redaction protocols, ensuring that sensitive context, proprietary tooling outputs, and unredacted prompts are never exposed [S2][S3][S4][S6]. Failed and invalid execution runs are explicitly preserved and labeled, as they serve as critical indicators of model instability, edge-case failures, and system reliability limits [S8]. 

Metric reporting is standardized to ensure cross-model comparability, requiring the consistent tracking of pass rates, invalid-run counts, median generation throughput, speculative decoding efficiency, token utilization, and output artifacts [S5]. All public disclosures must utilize redacted summaries and sanitized synthetic prompts, stripping any real-world identifiers, communication logs, or cryptographic secrets before export [S4][S6]. WorkDash-derived artifacts remain strictly confined to the home lab environment, forming an internal-only telemetry layer that supports debugging without external exposure [S3]. 

This policy document operationalizes these principles into actionable procedures, compliance checklists, metric definitions, and reporting templates. It ensures that the AI Flight Recorder lab maintains scientific rigor, respects privacy boundaries, and produces publishable benchmark data that is both technically accurate and governance-compliant. Every procedural step, retention rule, and disclosure standard is traceable to the provided synthetic sources, with conflicts resolved through explicit recency and specificity hierarchies.

# Source Priority

The resolution of policy directives follows two explicit governance rules: newer policy supersedes older policy, and more specific policy supersedes general policy. Applying these rules to the provided synthetic sources establishes the following precedence hierarchy:

1. **[S8 2026-06]** – *Highest Priority (Newest & Specific)*: Mandates retention and labeling of failed/invalid runs for reliability tracking. Overrides any older guidance on discarding failures.
2. **[S6 2026-05]** – *High Priority (Newest & Specific)*: Dictates local retention of raw private prompts until explicit deletion and requires redacted summaries for publishable reports. Overrides general retention rules for raw prompts.
3. **[S5 2026-04]** – *High Priority (Newest & Specific)*: Defines exact metrics required for model comparisons. Takes precedence over vague or outdated reporting guidelines.
4. **[S4 2026-03]** – *Medium-High Priority (Newer & Specific)*: Establishes export conditions for synthetic prompts, requiring sanitization of real names, emails, Teams messages, and secrets. Overrides blanket export permissions.
5. **[S3 2026-01]** – *Medium Priority (Newer & Specific)*: Strictly confines WorkDash-derived artifacts to the home lab. Overrides any general screenshot or artifact publication rules.
6. **[S2 2025-05]** – *Medium Priority (Older but Specific)*: Requires public screenshots to include model name, quantization, context size, and token counts, while permitting omission of raw prompts. Applies only to sanitized, non-WorkDash materials.
7. **[S1 2024-11]** – *Low Priority (Oldest & General)*: Establishes a baseline 14-day retention period for all benchmark artifacts. Applies only where not overridden by newer or more specific directives.
8. **[S7 2025-08]** – *Lowest Priority (Draft & Overridden)*: Suggests discarding all failed runs. Explicitly overridden by S8 (newer, specific, and non-draft).

This hierarchy ensures that policy evolution is respected, with the most recent and contextually precise directives governing operational behavior. General rules like S1 serve as fallbacks, while specific, newer rules like S6, S8, and S3 dictate actual execution. The priority order directly informs the conflict resolution process and the final policy synthesis.

# Resolved Policy

The resolved policy synthesizes the priority hierarchy into actionable governance rules. Each rule is traceable to specific sources and reflects the applied conflict resolution logic.

**1. Artifact Lifecycle & Retention**
- General benchmark artifacts (logs, intermediate outputs, configuration snapshots) must be retained for a minimum of 14 days before archival or secure deletion [S1].
- Raw private prompts are exempt from the 14-day baseline and must be retained locally until explicitly deleted by the operator [S6].
- All retained artifacts must be stored in isolated, access-controlled directories to prevent accidental exposure [S3][S6].
- Retention periods may be extended for artifacts required for long-term reliability analysis or comparative benchmarking, provided they remain within the home lab boundary [S1][S6].

**2. Prompt Handling & Export Protocol**
- Synthetic benchmark prompts may be exported for public reporting only if they contain zero real names, email addresses, Teams messages, or cryptographic secrets [S4].
- Raw private prompts must never be published in full; publishable reports must utilize redacted summaries that preserve technical intent while stripping sensitive context [S6].
- Exported prompts must be validated against a sanitization checklist before leaving the home lab environment [S4].
- Any prompt containing PII, communication logs, or secrets must be quarantined and processed through the redaction pipeline before any external sharing [S4][S6].

**3. Public Reporting & Screenshot Standards**
- Public screenshots may omit raw prompts but must explicitly display model name, quantization format, context size, and token counts [S2].
- All public materials must include a metadata header confirming compliance with sanitization and retention policies [S2][S4][S6].
- Screenshots must not capture WorkDash-derived artifacts, internal telemetry dashboards, or unredacted prompt contexts [S3].
- Public reports must clearly distinguish between internal debugging data and publishable benchmark results [S6].

**4. Failure Tracking & Reliability Reporting**
- Failed and invalid execution runs must be retained and clearly labeled to document reliability problems, edge-case failures, and system instability [S8].
- Failed runs must not be discarded or hidden, as they provide critical insights into model robustness and benchmarking infrastructure limits [S8].
- Failure labels must include run ID, failure type, timestamp, and sanitized context summary for internal tracking [S8].
- Public reports may reference failure rates but must not expose raw failure prompts or unredacted error logs [S6][S8].

**5. Comparison Metric Standardization**
- Model comparison reports must consistently report pass rate, invalid-run count, median generation TPS, MTP acceptance, reasoning tokens, final tokens, and output artifacts [S5].
- Metrics must be calculated across standardized test suites to ensure cross-model comparability [S5].
- Output artifacts included in comparisons must be redacted or summarized per privacy guidelines before publication [S5][S6].
- All metrics must be accompanied by methodology notes explaining test conditions, hardware configuration, and sampling parameters [S5].

**6. WorkDash & Internal Telemetry Boundaries**
- WorkDash-derived artifacts must never be published outside the home lab under any circumstances [S3].
- Internal telemetry may be used for debugging, performance profiling, and reliability analysis, but must remain strictly isolated from public-facing materials [S3].
- Any accidental exposure of WorkDash data must trigger an immediate containment protocol and policy review [S3].

# Contradictions

The synthetic sources contain explicit and implicit conflicts that require resolution through the established priority rules. Each contradiction is analyzed below with its resolution rationale.

**1. Failed Run Handling: S7 vs. S8**
- *Conflict*: S7 (2025-08) states that a draft policy requires all failed runs to be discarded. S8 (2026-06) explicitly mandates that failed and invalid runs should be retained and clearly labeled because they reveal reliability problems.
- *Resolution*: S8 is newer (2026-06 vs. 2025-08) and more specific to reliability tracking. Additionally, S7 is explicitly labeled as a "draft," which carries lower governance weight than finalized directives. S8 fully overrides S7. Failed runs are retained and labeled.

**2. Artifact Retention: S1 vs. S6**
- *Conflict*: S1 (2024-11) establishes a blanket 14-day retention period for all benchmark artifacts. S6 (2026-05) specifies that raw private prompts should be retained locally until explicitly deleted.
- *Resolution*: S6 is newer and more specific to raw private prompts. The 14-day rule in S1 applies to general artifacts (logs, configs, intermediate outputs), while S6 overrides retention for raw prompts. Raw prompts follow the explicit-deletion protocol; other artifacts follow the 14-day baseline.

**3. Public Screenshot Requirements: S2 vs. S3 vs. S4**
- *Conflict*: S2 (2025-05) permits public screenshots with specific metadata but allows omission of raw prompts. S3 (2026-01) forbids publishing any WorkDash-derived artifacts. S4 (2026-03) allows exporting synthetic prompts if sanitized.
- *Resolution*: S3 and S4 are newer and more specific. Public screenshots must comply with S2's metadata requirements but must exclude WorkDash content per S3. Synthetic prompts may be included in public materials only if fully sanitized per S4. S2's permission to omit raw prompts aligns with S6's redaction requirement.

**4. Output Artifacts in Comparisons: S5 vs. S6**
- *Conflict*: S5 (2026-04) requires reporting output artifacts for model comparisons. S6 (2026-05) mandates that publishable reports use redacted summaries.
- *Resolution*: S6 is newer and more specific regarding privacy. Output artifacts must be included in comparisons per S5, but must be redacted or summarized before publication per S6. Raw output artifacts may be retained internally for debugging, but public disclosures must use sanitized versions.

**5. Draft vs. Finalized Directives: S7 vs. All Others**
- *Conflict*: S7 is explicitly a draft policy suggesting failed run discarding. All other sources are finalized directives.
- *Resolution*: Draft policies carry no binding authority once superseded by finalized rules. S7 is functionally nullified by S8 and the broader finalized policy framework.

All contradictions are resolved through strict application of recency and specificity rules, ensuring a coherent, non-contradictory policy baseline.

# Metrics To Report

Standardized metric reporting is essential for reproducible, cross-model benchmarking. The following metrics must be consistently tracked, calculated, and reported in all model comparison outputs [S5]. Each metric is defined with its calculation methodology, reporting format, and governance rationale.

**1. Pass Rate**
- *Definition*: The percentage of benchmark runs that successfully complete validation checks without triggering failure states or invalid outputs.
- *Calculation*: `(Successful Runs / Total Runs) × 100`
- *Reporting Format*: Percentage with two decimal places (e.g., 87.34%).
- *Rationale*: Provides a high-level reliability indicator. Must be reported alongside invalid-run counts to contextualize success rates [S5][S8].

**2. Invalid-Run Count**
- *Definition*: The absolute number of execution attempts that fail validation, timeout, crash, or produce structurally invalid outputs.
- *Calculation*: Direct count of runs flagged as invalid during post-processing.
- *Reporting Format*: Integer with clear labeling (e.g., `Invalid Runs: 12`).
- *Rationale*: Critical for reliability analysis. Retained and labeled per S8 to expose system instability and edge-case failures [S5][S8].

**3. Median Generation TPS**
- *Definition*: The median tokens-per-second rate during the generation phase, excluding prefill and idle periods.
- *Calculation*: Median of per-run generation throughput values.
- *Reporting Format*: Decimal with one place (e.g., `42.1 TPS`).
- *Rationale*: Median is preferred over mean to resist outlier skew from hardware throttling or speculative decoding spikes. Reflects consistent model performance [S5].

**4. MTP Acceptance**
- *Definition*: The Multi-Token Prediction acceptance rate, measuring how often speculative decoding tokens are accepted by the target model.
- *Calculation*: `(Accepted Speculative Tokens / Total Speculative Tokens) × 100`
- *Reporting Format*: Percentage with one decimal place (e.g., `68.2%`).
- *Rationale*: Indicates efficiency of speculative decoding pipelines. Higher acceptance correlates with faster effective throughput and reduced compute waste [S5].

**5. Reasoning Tokens**
- *Definition*: The count of tokens generated during internal reasoning, chain-of-thought, or planning phases before the final answer.
- *Calculation*: Tracked via model output segmentation or internal telemetry counters.
- *Reporting Format*: Integer (e.g., `3,100`).
- *Rationale*: Distinguishes between deliberative and direct generation. Useful for analyzing model behavior patterns and compute allocation [S5].

**6. Final Tokens**
- *Definition*: The total number of tokens in the final output, including reasoning, formatting, and answer text.
- *Calculation*: Sum of all generated tokens per run.
- *Reporting Format*: Integer (e.g., `1,105`).
- *Rationale*: Indicates context window utilization and output verbosity. Helps normalize performance metrics across varying output lengths [S5].

**7. Output Artifacts**
- *Definition*: The raw generated text, code, or structured data produced by the model during benchmark execution.
- *Reporting Format*: Sanitized excerpts or redacted summaries in public reports; full artifacts retained internally [S5][S6].
- *Rationale*: Provides qualitative validation of quantitative metrics. Must comply with redaction and export guidelines before public disclosure [S5][S6].

All metrics must be reported in standardized tables with clear methodology notes, hardware specifications, and sampling parameters to ensure reproducibility [S5]. Internal dashboards may display raw values, but public reports must use aggregated, redacted, or summarized formats per privacy directives [S6].

# What Must Stay Private

Privacy boundaries are strictly enforced to protect sensitive context, proprietary tooling outputs, and internal telemetry. The following categories must never leave the home lab environment unless explicitly sanitized and approved per policy.

**1. Raw Private Prompts**
- Raw prompts must be retained locally until explicitly deleted by the operator [S6].
- They must never be published in full, even in internal archives, without access controls and explicit retention justification [S6].
- Any prompt containing PII, communication logs, or secrets must be quarantined and processed through the redaction pipeline [S4][S6].

**2. WorkDash-Derived Artifacts**
- All artifacts generated by or exported from WorkDash must never be published outside the home lab [S3].
- This includes telemetry dashboards, internal logs, performance profiles, and debugging snapshots [S3].
- WorkDash data may be used for internal reliability analysis but must remain strictly isolated from public-facing materials [S3].

**3. PII, Secrets, and Communication Logs**
- Real names, email addresses, Teams messages, cryptographic keys, API tokens, and infrastructure credentials must be stripped before any export [S4].
- Synthetic prompts must be validated against a sanitization checklist to ensure zero real-world identifiers [S4].
- Any accidental exposure triggers immediate containment, redaction, and policy review [S4][S6].

**4. Unredacted Summaries & Internal Debugging Notes**
- Publishable reports must use redacted summaries that preserve technical intent while stripping sensitive context [S6].
- Internal debugging notes, failure annotations, and raw error logs must not be included in public disclosures [S6][S8].
- Public materials must clearly distinguish between internal telemetry and publishable benchmark results [S6].

**5. Failed Run Context**
- While failed runs are retained and labeled for reliability tracking [S8], their raw prompt context must be redacted before any external sharing [S6][S8].
- Failure labels may include run ID, failure type, and sanitized summary, but must not expose unredacted prompts or internal tooling outputs [S8].

**Compliance Checklist for Privacy Enforcement**
- [ ] Verify raw prompts are retained locally until explicit deletion [S6]
- [ ] Confirm WorkDash artifacts are excluded from all public materials [S3]
- [ ] Validate synthetic prompts against PII/secret sanitization rules [S4]
- [ ] Ensure publishable reports use redacted summaries [S6]
- [ ] Label failed runs internally without exposing raw context externally [S8]
- [ ] Apply access controls to all retained artifact directories [S3][S6]

# Example Report Language

The following examples demonstrate compliant phrasing for internal logs, public reports, redaction notices, and methodology sections. All language aligns with the resolved policy and source directives.

**Internal Telemetry Log Entry**
```
Run ID: 4821 | Timestamp: 2026-07-12T14:33:09Z | Status: FAILED
Failure Type: Validation Timeout | Invalid-Run Count: 12
Raw Prompt Retained Locally per S6 | Labeled for Reliability Analysis per S8
WorkDash Telemetry: Quarantined | No External Export
```

**Public Report Metadata Header**
```
Model: Llama-3.1-70B | Quant: Q4_K_M | Context: 128k | Tokens: 14,205
Pass Rate: 87.34% | Invalid Runs: 12 | Median TPS: 42.1
MTP Acceptance: 68.2% | Reasoning Tokens: 3,100 | Final Tokens: 1,105
All metrics calculated per S5. Public screenshots comply with S2 metadata requirements.
```

**Redaction & Sanitization Notice**
```
[REDACTED: Synthetic prompt sanitized per S4. Original contained placeholder entities only. No real names, emails, Teams messages, or secrets present. Raw private prompts retained locally until explicit deletion per S6. Publishable reports utilize redacted summaries per S6.]
```

**Methodology & Compliance Disclaimer**
```
Benchmark execution followed standardized test suites with consistent hardware configuration and sampling parameters. All failed and invalid runs are retained and clearly labeled to document reliability problems per S8. General benchmark artifacts are retained for 14 days per S1, except raw private prompts which are retained locally until explicitly deleted per S6. WorkDash-derived artifacts remain strictly within the home lab and are never published per S3. Public screenshots include model name, quantization, context size, and token counts per S2, while omitting raw prompts. Output artifacts are redacted or summarized before publication per S6. All synthetic prompts exported for public reporting contain no real names, emails, Teams messages, or secrets per S4.
```

**Failure Context Reporting (Public-Facing)**
```
Reliability Analysis: 12 invalid runs were identified across the benchmark suite. These runs were retained and labeled to expose edge-case failures and system instability per S8. Raw prompt context has been redacted for privacy compliance. Failure rates are reported alongside pass rates to provide transparent reliability metrics.
```

These examples ensure consistent, policy-compliant language across internal and public materials. They explicitly reference governance rules, maintain technical accuracy, and enforce privacy boundaries without compromising scientific transparency.

# Confidence

Confidence in this policy derivation is **95%**. The synthetic sources provide clear, dated directives that align well with standard benchmark governance practices. The conflict resolution rules (newer > older, specific > general) are explicitly defined and unambiguous, allowing for deterministic policy synthesis. S7 is explicitly labeled as a draft, which further reduces its weight and simplifies resolution. S6, S8, and S3 provide precise, non-overlapping boundaries for retention, failure tracking, and internal telemetry, respectively. S5 and S2 establish concrete reporting standards that are easily operationalized. 

The only minor uncertainty lies in the practical implementation of redaction workflows and sanitization validation, which would require additional procedural documentation beyond the scope of the provided sources. However, the policy framework itself is logically consistent, fully traceable to source IDs, and compliant with all stated governance rules. The resolved policy successfully balances transparency, reliability tracking, and privacy enforcement, making it highly suitable for the AI Flight Recorder home lab environment.