Decision Log: Benchmark Readiness for Self-Recognition Evolve, Retrieval Guardrails, and Knowledge Pack Expansion (2026-01-26, Slot 2)

Context#

Recent work focused on hardening the self-recognition evolve pipeline, curbing excessive retrieval, expanding domain-specific knowledge resources, and readying the system for more rigorous evaluation. Parallel efforts improved authentication, proxy/usage handling, pricing/plan APIs, and administrative surfaces. A daily reorganize routine using an NDC-based taxonomy was introduced to reveal knowledge biases and keep the corpus balanced.

What changed#

• Self-recognition evolve
• Iterative improvements across knowledge-pack integration and a multi-agent “universe” sampling environment.
• Unification efforts between diagnostic flows and the code-oriented self-recognition mode.
• A CI workflow was added to automate evolve runs aligned with the desire pipeline.
• Timeouts were extended to stabilize longer-running trials; parallel execution was explored and flagged for further testing.
• Retrieval guardrails
• Guardrails were introduced to address “too many retrievals,” with constraints aimed at reducing fan-out and improving determinism.
• Desire defaults
• The desire component now defaults to reading all relevant entries, simplifying configuration and ensuring broader coverage during evolve runs.
• Knowledge pack expansion
• Multiple new packs were added around mirror self-recognition (MSR) and adjacent domains, including:
• Ethics and compliance frameworks for animal-based MSR studies.
• Historical evolution and cultural significance of mirrors.
• Industrial optics fundamentals, including mirror coating and apparatus design considerations for MSR setups.
• Cross-species self-awareness indicators and comparative syntheses of non-MSR approaches.
• The catalog and assignments were updated to reflect the expanded scope.
• Non-technical “universe” samples
• Expanded sample agents for foundational non-technical knowledge integration spanning ethics, creativity, leadership, culture/cross-culture, emotional and epistemic reasoning, learning, personal development, and orchestration/root aggregation.
• Platform and product surfaces
• Authentication for command-line workflows was refined.
• AI proxy handling and usage tracking cores were updated for stability and clearer accounting.
• Pricing and plan logic were refined; user-facing APIs around plans and profiles received updates.
• Administrative dashboards were adjusted to reflect recent product directions.
• Reorganize and data hygiene
• A daily reorganize job was introduced, leveraging an NDC-based taxonomy to surface knowledge imbalance and guide content curation.
• JSON formatting conventions were standardized to reduce merge conflicts.

Decisions#

• Continue evolving self-recognition with integrated knowledge packs and multi-agent sampling; keep extended timeouts while validating parallel execution.
• Enforce retrieval guardrails to limit fan-out; treat configuration as tunable and monitor for regressions.
• Keep desire’s read-all-by-default setting to maximize signal during evolve cycles.
• Prioritize knowledge pack domains tied to MSR: ethics/compliance, historical context, industrial optics, and cross-species indicators.
• Maintain the daily reorganize routine to identify and mitigate knowledge bias via an NDC-aligned taxonomy.
• Proceed with unification of diagnostic and code-oriented self-recognition commands.
• Keep the automated evolve workflow in CI to standardize evaluation cadence.
• Finalize updates across authentication, proxy, usage accounting, pricing/plans, and administrative surfaces for enterprise readiness.

Benchmark readiness focus#

Without adopting external datasets or benchmarks, readiness will be assessed via internal criteria:

• Evolve stability: completion rate, timeout incidence, and failure modes before/after guardrails.
• Retrieval discipline: average retrieval count per task, tail behavior, and impact on output quality.
• Knowledge coverage: topic distribution against the NDC-based taxonomy; detection and remediation of domain imbalance.
• Orchestration performance: latency and determinism across multi-agent sampling flows.
• Platform health: authentication success rates, proxy/usage error budgets, and API response health for pricing/plans and profiles.

Risks and mitigations#

• Over-throttling retrieval may degrade answer quality.
• Mitigation: expose guardrail knobs, monitor quality regressions, iterate thresholds.
• Parallelism can surface race conditions and flakiness.
• Mitigation: keep timeouts conservative, expand test coverage, stage rollouts.
• Knowledge pack sprawl increases maintenance overhead.
• Mitigation: enforce curation via the daily reorganize routine and taxonomy checks.
• Ethics/compliance scope creep without clear boundaries.
• Mitigation: codify inclusion criteria and document jurisdiction-aware considerations within packs.

Next steps#

• Validate parallel execution paths under extended timeouts; add targeted test cases.
• Tune retrieval guardrails with real workloads; add observability for fan-out and quality impact.
• Continue enriching MSR-adjacent knowledge packs with curated, taxonomy-checked content.
• Expand non-technical agent samples to cover remaining reasoning facets and collaboration patterns.
• Operationalize the CI evolve workflow on a predictable schedule and publish run summaries.
• Monitor authentication, proxy, usage, and plan/pricing endpoints for error budgets and latency.
• Iterate the reorganize job to flag high-variance domains and suggest targeted content acquisition.