5 papers
Beyond Endpoint Performance: Process-Level Evaluation of Self-Evolving Agents
Hongqiang Lin, Chao Liu, Xiaofan Bai +4
Self-evolving agents convert interaction feedback into persistent artifacts, such as memories or skills, which in turn guide subsequent decisions. As these artifacts are iterativel…
SkillZip Pro: Execution-Aware Dynamic Compression of Progressively Loaded Skills for Self-Evolving Agents
Xiaofan Bai, Chao Liu, Hongqiang Lin +5
Production agent skills are directory bundles, not isolated prompts. The root is loaded at activation; references, schemas, scripts, assets, and nested subskills are loaded only wh…
Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting
Hongqiang Lin, Chao Liu, Xiaofan Bai +4
Enabling large language model (LLM) agents to accumulate and reuse experience from past interactions remains a central challenge in real-world applications. A promising solution is…
SkillZip: Evaluation-Free Skill Compression for Self-Evolving Agents by Discovering Reusable Structure
Xiaofan Bai, Hongqiang Lin, Chao Liu +4
Self-evolving agents accumulate reusable skills by appending successful procedures and failure fixes. Over time, the same requirement is often restated in several branches, example…
Targeted Counterfactual Fingerprinting for Black-Box LLM Ownership Verification
Yutong Wu, Xiaofan Bai, Shixin Li +10
Large language models (LLMs) are high-value assets that can be derived through redeployment, fine-tuning, quantization, or further alignment. Because deployed LLMs are commonly exp…