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Self-evolving agents convert interaction feedback into persistent artifacts, such as memories or skills, which in turn guide subsequent decisions. As these artifacts are iteratively updated throughout an experience stream, the capabilities they support may evolve. Consequently, endpoint performance alone offers an inco...
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 exposed only through query APIs, ownership verification must often rely on black-box text responses. This setting is difficult: generations are...
This work introduces \method, an evaluation-free compressor for complete, progressively loaded skill bundles, which leaves the agent harness unchanged and emits an ordinary directory and preserves routing, so every required file and directly callable entry remains reachable after rewriting.
SkillZip is presented, an evaluation-free method that compresses a skill by finding its shortest faithful structural explanation, subject to a hard coverage constraint for every extracted trigger, workflow edge, tool requirement, obligation, and output field.
SkillBoost is proposed, a three-stage framework that mitigates both risks: structured exploitation localizes observed failures to editable skill components, prior-guided exploration draws on prior knowledge in the LLM to generate diverse repair candidates, and verified acceptance commits a candidate only when it improv...
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