HiSME lets LLM agents evolve the way they evolve skills, without changing weights
机器之心 · wechat · 2026-07-23
A Tsinghua University and Huawei team introduces HiSME, a lightweight hierarchy for skill meta-evolving in LLM agents.
Core idea
- Agents first turn execution traces into reusable skills.
- HiSME adds a second layer: it also evolves the process that generates and maintains those skills.
- The system keeps everything in text space, without updating model weights.
How it works
- Extractor finds candidate skills from single trajectories.
- Refactorer abstracts shared structure across multiple traces or existing skills.
- Refiner / filter repair, disable, or keep skills based on usefulness and reuse.
- A metarefiner summarizes feedback into meta-skills that are written back into the prompts of the skill pipeline.
Results
- Evaluated on BFCL-v3 multi-turn tool use and MineDojo.
- It outperforms no-skill baselines, static skill evolution, and other test-time learning methods such as AWM and Memento.
- In MineDojo, better skills reduce wasted exploration and token cost.
- A meta-test shows that frozen meta-skills transfer well to new static runs.
Why it matters
The paper frames agent optimization as something broader than model fine-tuning: prompts, tool routing, memory, evaluators, and skill maintenance can all become evolvable components.
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