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The author shares insights after trying various Agent Memory implementations, concluding that only strictly length-limited entry-level memory (like Hermes) and skill evolution based on trajectory precipitation are somewhat useful, while other graph-based or card-based methods are ineffective.
SkillsVote is a governance framework for long-horizon LLM agents that manages reusable skills through structured collection, recommendation, and evolution, improving performance on Terminal-Bench 2.0 and SWE-Bench Pro without model updates.
SkillFlow proposes a flow-driven recursive skill evolution framework for LLM-based agentic orchestration, using Tempered Trajectory Balance to prevent strategy collapse and provide transparent credit assignment. Experiments on 14 datasets show significant improvements over baselines in QA, math, code, and decision-making tasks.
SkillClaw introduces a framework for collective skill evolution in multi-user LLM agent systems, enabling autonomous updates and cross-user knowledge transfer by aggregating interactions and feedback to improve performance across the ecosystem.