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MetaSkill-Evolve introduces a recursive two-timescale framework for LLM agents to evolve both task skills and the improvement procedure itself, achieving notable accuracy gains on OfficeQA, SealQA, and ALFWorld benchmarks.
The article discusses using Gemini Omni's edit mode as a deterministic transformation step in agent video pipelines, enabling reproducible state changes without full video regeneration, improving cost and speed.
This paper identifies a 'Diagnostic Paradox' in multi-module LLM agents: the module most causally responsible for failures (the routing module) is not the best place to intervene, and patching it can harm performance. The authors propose the 'Linguistic Contract' hypothesis and present empirical evidence across three agent families.
A routing layer that automatically selects the best LLM per request based on priority flags (speed, cost, quality, balanced) using a weighted score, with under 1ms decision time and built-in fallback, caching, and metrics.