AEL uses a fast-timescale bandit for memory policy selection and slow-timescale LLM reflection for causal insights, achieving a Sharpe ratio of 2.13 on a 208-episode portfolio benchmark while showing that added mechanisms degrade performance.
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The Experience Compression Spectrum unifies memory, skills, and rules in LLM agents along increasing compression levels and identifies the absence of adaptive cross-level compression as the missing diagonal.
SkillMAS couples skill evolution and MAS restructuring via utility learning from traces, bounded skill updates, and evidence-gated team changes, reporting competitive results across manipulation, CLI, and retail tasks.
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AEL: Agent Evolving Learning for Open-Ended Environments
AEL uses a fast-timescale bandit for memory policy selection and slow-timescale LLM reflection for causal insights, achieving a Sharpe ratio of 2.13 on a 208-episode portfolio benchmark while showing that added mechanisms degrade performance.
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Experience Compression Spectrum: Unifying Memory, Skills, and Rules in LLM Agents
The Experience Compression Spectrum unifies memory, skills, and rules in LLM agents along increasing compression levels and identifies the absence of adaptive cross-level compression as the missing diagonal.
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SkillMAS: Skill Co-Evolution with LLM-based Multi-Agent System
SkillMAS couples skill evolution and MAS restructuring via utility learning from traces, bounded skill updates, and evidence-gated team changes, reporting competitive results across manipulation, CLI, and retail tasks.