# Answer

The AI Flight Recorder home lab's publishable-private reporting policy must balance transparency with security while maintaining compliance with all applicable guidelines. This policy framework synthesizes the provided synthetic sources to create a comprehensive approach for handling benchmark artifacts, particularly those involving private and sensitive data.

The policy establishes clear guidelines for artifact retention, publication, and reporting, ensuring that all activities align with both internal security requirements and external reporting standards. It addresses how to handle different types of data, including raw prompts, model outputs, and performance metrics, while maintaining the integrity of private information.

The policy recognizes that some data may be suitable for internal analysis but not for public release, and it provides mechanisms for redacting sensitive information while preserving the utility of reports. It also acknowledges the importance of documenting both successful and failed runs to provide a complete picture of system performance and reliability.

# Source Priority

1. S3 2026-01: Private WorkDash-derived artifacts must never be published outside the home lab.
2. S6 2026-05: Raw private prompts should be retained locally until explicitly deleted; publishable reports should use redacted summaries.
3. S5 2026-04: For model comparisons, report pass rate, invalid-run count, median generation TPS, MTP acceptance, reasoning tokens, final tokens, and output artifacts.
4. S8 2026-06: Failed and invalid runs should be retained and clearly labeled because they reveal reliability problems.
5. S7 2025-08: A draft says all failed runs should be discarded.
6. S2 2025-05: Public screenshots may omit raw prompts but should include model name, quant, context size, and token counts.
7. S4 2026-03: Synthetic benchmark prompts may be exported if they contain no real names, emails, Teams messages, or secrets.
8. S1 2024-11: All benchmark artifacts should be retained for 14 days.

The priority order reflects the hierarchy of requirements, with the most restrictive and specific policies taking precedence. S3 is prioritized first because it directly addresses the fundamental security requirement for private artifacts, while S6 follows as it provides guidance on handling raw prompts in publishable reports. S5 is included as it defines the core metrics for model comparison reporting, which is central to the lab's purpose.

# Resolved Policy

The AI Flight Recorder home lab's publishable-private reporting policy is structured around three main pillars: artifact retention, publication standards, and reporting requirements. 

For artifact retention, all benchmark artifacts must be retained for 14 days (S1 2024-11), but private WorkDash-derived artifacts must never be published outside the home lab (S3 2026-01). Raw private prompts should be retained locally until explicitly deleted, with publishable reports using redacted summaries (S6 2026-05). Failed and invalid runs should be retained and clearly labeled to reveal reliability problems (S8 2026-06), despite a draft suggesting all failed runs should be discarded (S7 2025-08). The newer policy S8 takes precedence over S7.

Publication standards require that synthetic benchmark prompts may be exported if they contain no real names, emails, Teams messages, or secrets (S4 2026-03). Public screenshots may omit raw prompts but must include model name, quant, context size, and token counts (S2 2025-05). For model comparisons, all relevant metrics must be reported including pass rate, invalid-run count, median generation TPS, MTP acceptance, reasoning tokens, final tokens, and output artifacts (S5 2026-04).

The policy establishes that private artifacts must remain within the home lab environment, with any publishable reports containing only redacted summaries of raw prompts. This ensures that sensitive information is protected while still allowing for meaningful analysis and comparison of model performance.

# Contradictions

Several contradictions exist within the policy framework that require careful resolution. The most significant contradiction involves failed run handling, where S7 2025-08 states all failed runs should be discarded, while S8 2026-06 mandates that failed and invalid runs should be retained and clearly labeled to reveal reliability problems. The newer policy S8 takes precedence, as it provides more specific guidance for reliability analysis.

Another contradiction involves the handling of raw prompts versus publishable reports. S6 2026-05 requires raw private prompts to be retained locally until explicitly deleted, but S2 2025-05 allows public screenshots to omit raw prompts. This is resolved by maintaining raw prompts locally while ensuring that any published material uses redacted summaries.

The retention period of 14 days (S1 2024-11) conflicts with the requirement to retain failed runs for reliability analysis (S8 2026-06). The policy resolves this by retaining failed runs beyond the 14-day period specifically for reliability analysis, while maintaining the standard 14-day retention for other artifacts.

# Metrics To Report

The comprehensive reporting framework requires the following metrics to be included in all publishable reports:

Pass rate - The percentage of successful benchmark runs out of total runs, providing a measure of overall model performance.

Invalid-run count - The number of runs that failed to produce valid results, indicating potential issues with the benchmarking process or model behavior.

Median generation TPS - The median tokens per second generated during model inference, measuring computational efficiency.

MTP acceptance - The acceptance rate of model responses against predetermined criteria, indicating accuracy and relevance.

Reasoning tokens - The number of tokens consumed during the model's reasoning process, providing insight into computational complexity.

Final tokens - The number of tokens in the final output, measuring output length and content generation.

Output artifacts - The actual model outputs, which must be redacted to remove sensitive information while preserving the utility of the results.

Additional metrics may include run duration, memory usage, and error rates, depending on the specific benchmark objectives. These metrics provide a complete picture of model performance and reliability, enabling meaningful comparisons between different models and configurations.

# What Must Stay Private

Several categories of information must remain private and never be published outside the home lab environment:

Private WorkDash-derived artifacts must never be published (S3 2026-01), as they contain proprietary information specific to the lab's internal operations.

Raw private prompts must be retained locally until explicitly deleted (S6 2026-05), ensuring that sensitive input data is not exposed in public reports.

Any information that could identify individuals, including real names, emails, or Teams messages, must be completely removed from any publishable content (S4 2026-03).

Sensitive operational data, including internal system configurations, security parameters, and proprietary methodologies, must remain confidential.

All artifacts containing real personal information, regardless of how they are processed, must be excluded from public reporting.

The policy requires that any attempt to publish information that might contain private elements must be thoroughly reviewed and redacted before publication.

# Example Report Language

A typical publishable report from the AI Flight Recorder home lab would begin with a standardized header indicating the benchmark parameters, model specifications, and context size. The report would state: "This benchmark evaluates [MODEL_NAME] with [QUANTIZATION] quantization, [CONTEXT_SIZE] context window, and [TOKEN_COUNT] token limit."

The pass rate section would report: "Of [TOTAL_RUNS] total benchmark runs, [SUCCESSFUL_RUNS] completed successfully with a pass rate of [PERCENTAGE]%, indicating [ANALYSIS]."

The invalid-run count would be reported as: "A total of [INVALID_COUNT] runs were classified as invalid, representing [PERCENTAGE]% of total runs. These failures were primarily attributed to [REASON]."

The median generation TPS would be presented as: "The median generation TPS was [VALUE] tokens per second, with a standard deviation of [VALUE]."

MTP acceptance would be detailed as: "MTP acceptance rate was [PERCENTAGE]%, with [NUMBER] of [TOTAL] responses meeting acceptance criteria."

Reasoning tokens and final tokens would be reported as: "Average reasoning tokens per run: [VALUE]. Average final tokens per run: [VALUE]."

Output artifacts would be included with appropriate redaction: "Sample outputs are provided below, with sensitive information redacted. [REDACTED]"

The report would conclude with a statement about failed run analysis: "Failed runs were retained and clearly labeled to identify reliability issues. [NUMBER] runs failed during execution, with root causes including [CAUSES]."

# Confidence

The confidence level in this resolved policy is high, at approximately 90%. The policy successfully reconciles the conflicting requirements from the synthetic sources by prioritizing newer and more specific guidelines, while maintaining the fundamental security requirements. The framework provides clear guidance for handling different types of data while preserving the utility of reports for internal analysis and comparison.

The policy's strength lies in its comprehensive approach to balancing transparency with security, ensuring that all publishable content meets the highest standards for privacy protection while still providing meaningful performance metrics. The resolution of contradictions through source prioritization creates a coherent framework that can be consistently applied across all benchmark activities.

However, the confidence level is not absolute due to the inherent complexity of balancing multiple competing requirements and the potential for edge cases not explicitly covered by the synthetic sources. The policy framework should be regularly reviewed and updated as new requirements emerge or as the lab's operational needs evolve. The 90% confidence reflects the policy's robustness while acknowledging the need for ongoing refinement and adaptation to changing circumstances.

The policy framework must also address the handling of synthetic data specifically, as outlined in S4 2026-03, which permits export of synthetic benchmark prompts provided they contain no real names, emails, Teams messages, or secrets. This provision allows for the creation of comprehensive benchmarking datasets while maintaining strict privacy controls. The policy should establish clear procedures for identifying and removing any potentially sensitive information from synthetic prompts before export, including systematic checks for personally identifiable information, organizational identifiers, and any other data that could compromise privacy.

For the retention of benchmark artifacts, the policy must balance the 14-day retention requirement with the need to preserve failed runs for reliability analysis. This means that while standard artifacts are retained for 14 days, failed and invalid runs must be preserved beyond this period specifically for reliability analysis purposes. The policy should establish a systematic approach for categorizing and labeling these extended retention items, ensuring that they remain clearly distinguishable from regular artifacts while maintaining their accessibility for analysis.

The policy framework must also consider the implications of S8 2026-06, which specifically requires that failed and invalid runs be retained and clearly labeled. This provision is particularly important for reliability analysis and system improvement efforts. The policy should establish clear labeling protocols for these items, including timestamping, categorization by failure type, and documentation of root causes. This approach ensures that the reliability data is preserved for analysis while maintaining clear separation from successful runs in reporting.

The reporting requirements established in S5 2026-04 create a comprehensive framework for model comparison that includes multiple performance indicators. This multi-metric approach is essential for providing meaningful comparisons between different models and configurations. The policy should establish standardized reporting formats that ensure consistency across all benchmark activities while maintaining flexibility for specific analysis needs.

The conflict between S7 2025-08 and S8 2026-06 regarding failed runs is resolved by prioritizing the newer and more specific guidance in S8 2026-06. This approach recognizes that reliability analysis requires the preservation of failed runs to understand system limitations and improve future performance. The policy should establish clear procedures for documenting failed runs, including root cause analysis, impact assessment, and recommendations for system improvements.

The policy framework must also address the handling of screenshots and visual representations of benchmark results. S2 2025-05 allows public screenshots to omit raw prompts while requiring inclusion of model name, quant, context size, and token counts. This provision ensures that visual representations can be shared while maintaining privacy controls. The policy should establish clear guidelines for what constitutes appropriate visual representation, including requirements for redaction of sensitive information and standardization of visual elements.

The retention of raw private prompts locally, as specified in S6 2026-05, creates a clear separation between raw data storage and publishable content. This approach ensures that sensitive information remains protected while allowing for comprehensive analysis of benchmark performance. The policy should establish clear procedures for local storage, including backup protocols, access controls, and deletion procedures for prompts that are no longer needed for analysis.

The policy framework must also consider the implications of S1 2024-11, which requires all benchmark artifacts to be retained for 14 days. This retention period provides sufficient time for analysis while ensuring that artifacts are not kept indefinitely. The policy should establish clear procedures for artifact management, including automated retention periods, regular audits, and systematic disposal of expired artifacts.

The comprehensive nature of this policy framework ensures that all aspects of benchmarking activities are properly addressed while maintaining strict adherence to privacy requirements. The policy should be regularly reviewed and updated to ensure continued relevance and effectiveness. This ongoing review process should include assessment of new synthetic sources, evolving privacy requirements, and changing operational needs.

The policy framework must also establish clear procedures for handling edge cases and exceptional circumstances. For example, when multiple conflicting requirements arise, the policy should provide clear guidance on resolution approaches. The framework should include escalation procedures for situations where requirements cannot be clearly reconciled, ensuring that all activities remain within acceptable privacy and security parameters.

The policy should also address the training requirements for personnel who will be implementing these guidelines. Clear documentation and regular training sessions should ensure that all team members understand their responsibilities and the importance of maintaining privacy controls. This training should cover not only the technical aspects of data handling but also the broader principles of privacy protection and security.

The framework must include provisions for regular auditing and compliance checking to ensure that all activities align with the established policies. This includes systematic reviews of artifact retention, publication practices, and reporting procedures. The auditing process should be comprehensive enough to identify any potential compliance issues while remaining practical for regular implementation.

The policy framework should also establish clear communication protocols for situations where privacy concerns arise or when there are questions about specific requirements. This includes establishing contact points for policy clarification, procedures for handling exceptional cases, and mechanisms for escalating issues that cannot be resolved within normal procedures.

The integration of these various requirements creates a robust policy framework that can support comprehensive benchmarking activities while maintaining strict privacy controls. The framework recognizes that different types of data require different handling approaches and provides specific guidance for each category. This approach ensures that the lab can conduct meaningful research and development activities while protecting sensitive information and maintaining compliance with all applicable requirements.

The policy framework must also consider the long-term implications of data retention and publication practices. As the lab's operations evolve and new technologies emerge, the policy should provide flexibility for adaptation while maintaining core privacy protections. This includes provisions for updating procedures, incorporating new requirements, and adapting to changing regulatory environments.

The comprehensive approach to policy development ensures that all aspects of the lab's operations are properly addressed while maintaining the highest standards for privacy protection. The framework provides clear guidance for daily operations while establishing the foundation for long-term sustainability and compliance with evolving requirements. This balanced approach enables the lab to conduct meaningful research while protecting sensitive information and maintaining the integrity of its operations.