6 papers
SkillNEO: Non-Elitist Evolutionary Optimization of Agent Skills
Enze Ma, Chunyu Miao, Yankai Chen +14
Agent skills turn procedural knowledge into reusable artifacts, allowing LLM agents to accumulate and refine expertise through experience. Building on this, skill evolution iterati…
Sycophancy Suppression Can Impair Rational Updating: Anti-Sycophancy Should Preserve the Ability to Update
Huanhuan Ma, Henry Peng Zou, Chengze Li +3
Large language models often exhibit sycophancy, revising their answers to align with users when users push back. Such answer flips, however, can arise from different causes. One po…
MemAudit: Auditing Long-Term Agent Memory via Hidden User-State Recovery
Enze Ma, Yufan Zhou, Wei-Chieh Huang +7
Long-term memory promises LLM agents that grow more capable across sessions, maintaining an accurate, evolving understanding of the user that interaction forms. In practice, howeve…
CoEvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification
Hanrong Zhang, Shicheng Fan, Henry Peng Zou +11
Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks that simple tool invocations cannot address. A tool is a single, self-contained func…
To See is Not to Master: Teaching LLMs to Use Private Libraries for Code Generation
Yitong Zhang, Chengze Li, Ruize Chen +4
Large Language Models (LLMs) have shown strong potential for code generation, yet they remain limited in private-library-oriented code generation, where the goal is to generate cod…
What Do Agents Learn from Trajectory-SFT: Semantics or Interfaces?
Weizheng Gu, Chengze Li, Zhuohao Yu +6
Large language models are increasingly evaluated as interactive agents, yet standard agent benchmarks conflate two qualitatively distinct sources of success: semantic tool-use and…