Rep2Skill: Representation-Guided Skill Self-Evolution for LLM Agents
cs.AI
Submitted: 2026-09-30
Updated: 2026-10-13
Sources
- SkillEval: Decomposing Agent Skill Quality into Interpretable Signals
- Interpreting and Controlling LLM Reasoning through Integrated Policy Gradient
- Emotion2Skill: Model-Internal Emotion Signals for Adaptive Skill Selection and Evolution
- The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets
- Skill-Pro: Learning Reusable Skills from Experience via Non-Parametric PPO for LLM Agents
- Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills
- Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- Emotion Concepts and their Function in a Large Language Model
- WikiSkill: Compiling Agent Experience into Persistent Knowledge for Skill Evolution
- Linear Representations of Sentiment in Large Language Models
- Agent Workflow Memory
- SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning
- Qwen3 Technical Report
- SkillOpt: Executive Strategy for Self-Evolving Agent Skills
- AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution
- Tracing Agentic Failure from the Flow of Success
- A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications
- Representation Engineering: A Top-Down Approach to AI Transparency
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