arXiv (All)
open access
Rep2Skill: Representation-Guided Skill Self-Evolution for LLM Agents
Textual skills enable large language model (LLM) based agents to accumulate reusable procedural knowledge without updating model parameters. Yet existing skill evolution remains largely confined to the text space: an optimizer must diagnose success and failure patterns, and revise skills solely from
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Harnessing LLM Agents with Skill Programs#1130554
- SkillSpec: Consensus-Gated Agent Skill Evolution via Representation Specialization#1155639
- S3C-LLM: Skill-Code Guided Agentic Language Models for Spectrum-to-Structure Elucidation#622536
- Grounded Skill Synthesis from Code at Scale for Agentic Intelligence#972627
- CoSkill: Joint Reinforcement Learning of Reasoning and Meta-Skill Agents for Hierarchical Skill Evolution#999877
Record · ID 1144116
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive