Gemini

Lowering temperature from 0.7 to 0.0 eliminates stochastic reasoning errors and benchmark variance (which caused wide confidence intervals in Experiments #1-#3). Simultaneously enabling date_calc and text_inspect alongside calculator, upgrading memory retrieval to hybrid (topK=10), and adding a compact verification protocol in system.md for 0-indexed cycle/calendar offsets, sequential state tracking, and sequence-ordered memory resolution will directly fix calendar-cycle reasoning failures (like reasoning:05) while lifting tool_use and memory in a single high-signal, single-pass update.

The current genome lacks date_calc and text_inspect in tools.enabled, causing calendar and string inspection tasks to fail or exhaust output tokens via manual calculation (resulting in empty tail / missing ANSWER line). Furthermore, memory retrieval is limited to topK=3 recency. By enabling all built-in computation tools, increasing maxOutputTokens and toolRetries, enabling formatRepair, upgrading memory retrieval to hybrid (topK=10), and adding concise instructions for direct tool usage (like single-expression (a^b)%m) and memory timestamp precedence, we can simultaneously lift tool_use and memory scores enough to clear the statistical significance threshold with minimal token overhead.

ENTITY_006 currently fails tool_use tasks requiring calendar arithmetic, string inspection, and modular exponentiation because only 'calculator' is enabled and maxToolCalls (4) or maxOutputTokens (700) are exhausted before emitting the final ANSWER line. Enabling 'date_calc', 'text_inspect', and 'run_javascript' (for BigInt modular arithmetic), increasing maxToolCalls to 6, enabling formatRepair, and adding concise tool-routing directives in system.md will resolve tool_use failures with minimal token overhead.

Memory is failing (40.0 on hidden, 83.3 on dev) strictly due to a retrieval bottleneck: memory tasks span 8 earlier conversations, yet config.json restricts retrieval to 'recency' with topK=3, blinding ENTITY_006 to the first 5 conversations. Furthermore, without explicit instructions on how to resolve contradicting memories, the agent may pick stale facts when corrections occur across conversations. Increasing topK to 12, enabling the built-in tools (including memory_search for explicit fallback and date_calc/text_inspect/run_javascript for tool_use tasks), lowering temperature to 0.0 for deterministic recall, and instructing the agent in system.md to prefer the most recent entry when memories conflict will resolve the memory deficit at near-zero additional token cost.