Benchmark Readiness Report: Self-Recognition Evolution, Retrieval Guardrails, and Knowledge-Pack Expansion (2026-01-26)

Context#

Since 2026-01-25 (Asia/Tokyo), 38 commits landed touching multiple areas:

• Iterative improvements to self-recognition evolution across content-driven and scenario-driven tracks.
• Retrieval tuning to mitigate over-fetching, plus attempts at parallelization (noted as requiring tests).
• Reliability hardening: error handling updates, timeout extensions, and removal of an unintended parameter.
• Admin and pricing-related API refinements, along with user profile and plan endpoints.
• CLI authentication client updates.
• Platform plumbing: AI proxy handling and usage accounting adjustments.
• Desire system behavior updates (read defaults refined) and integration work across related capabilities.
• Automated reorganization using a classification scheme to surface knowledge bias; associated data pipeline fixes.
• Generation of new knowledge packs covering topics such as mirror self-recognition fundamentals, non-mirror indicators of self-awareness, historical and cultural perspectives on mirrors, industrial optics considerations (including coatings), and ethics/compliance for animal studies.
• Expansion of sample multi-agent configurations for foundational, non-technical knowledge integration (e.g., creativity, critical thinking, ethics, leadership, cross-cultural communication, emotional and epistemic perspectives, knowledge and learning, personal development, orchestration/root, collaboration, communication, and conflict).
• CI workflow updates related to evolving self-recognition and desire.

Working directory note: a CI authentication configuration was adjusted (+5/−5), and a local credential artifact is untracked (ensure it remains excluded from version control).

Changes relevant to benchmarking#

• Throughput and reliability levers:
• Retrieval guardrails to reduce excessive results.
• Parallelization introduced but flagged for further testing.
• Timeout thresholds extended; error handling strengthened.
• Removal of an unintended parameter to prevent behavioral drift.
• Coverage levers:
• Substantial expansion of knowledge packs across technical, ethical, cultural, and historical facets of self-recognition.
• Nightly reorganization using a classification framework to detect and alleviate knowledge skew.
• Scenario levers:
• New sample multi-agent configurations broaden qualitative and scenario-based evaluation options across multiple competencies.
• Platform levers:
• Updates to AI proxy handling and usage accounting can affect measurement fidelity and logging pathways.
• Governance & CI:
• Workflow changes for self-recognition evolution and desire align automated checks with evolving evaluation targets.

Benchmark plan (no results yet)#

Given the above, focus on preparation and methodology rather than numbers:

• Define scenarios:
• Use the expanded multi-agent configurations to create representative evaluation scenarios (creativity, critical thinking, ethics, leadership, cross-culture, emotional/epistemic perspectives, collaboration/communication/conflict, etc.).
• Pair scenarios with relevant knowledge-pack domains (e.g., fundamentals, non-mirror indicators, cultural/historical, industrial optics, and ethics/compliance) to ensure coverage breadth.
• Parameter sweeps:
• Compare retrieval guardrail settings and timeout thresholds.
• Evaluate single-threaded vs. parallelized execution once stability is confirmed.
• Metrics to collect:
• Success/error rates and timeout incidence.
• Latency distributions and throughput under load.
• Scenario coverage balance pre/post nightly reorganization to monitor knowledge skew.
• Instrumentation & environment:
• Verify usage accounting paths and AI proxy behavior to ensure consistent logging.
• Keep admin/pricing/user endpoints stable across runs to avoid confounding changes.
• Confirm CI workflow gates for repeatable runs.

Risks and mitigations#

• Retrieval trimming may reduce recall:
• Mitigate via targeted guardrail ranges and scenario-specific overrides.
• Parallelization may introduce nondeterminism or race conditions:
• Stage with limited concurrency; lock critical sections and compare to single-thread baselines.
• Longer timeouts can hide systemic slowness:
• Track latency percentiles and time-to-first-byte; alert on regressions.
• Knowledge reorganization could shift scenario balance unexpectedly:
• Snapshot coverage before/after nightly runs; pin inputs for official benchmark windows.
• Local credentials and CI auth changes:
• Ensure sensitive artifacts remain untracked and rotate tokens if needed.

Next steps#

• Finalize the scenario set and parameter matrix.
• Land and validate parallelization safely, then re-run baselines.
• Align CI workflow checks with benchmark entry/exit criteria.
• Confirm platform instrumentation for consistent measurement.
• Freeze admin/pricing/user-facing behaviors during benchmark windows to maintain comparability.