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Self-evolving LLM agents promise to improve autonomously through continual interaction and learning, reducing their dependence on manually curated supervision. Realizing this promise requires not only updating the agent, but also evolving its training experience as its capabilities change. However, most existing pipeli...
This paper develops a measurement pipeline that recovers operational obligations, organizes their workflow context, maps testcases using both user prompts and initial resource states, and produces an auditable coverage report, which measures how completely a Skill’s test suite covers its operational test obligations.
A benchmark with tab-and-blank interlocking pieces where geometric constraints provide strong local compatibility requirements that, combined with visual content, yield unambiguous ground truth is introduced, establishing scalable geometric reasoning as an open challenge for vision-language models.
This work presents the first model extraction attack specifically designed for graph classification under strict black-box constraints, which uses model explanation outputs to guide Monte Carlo edge sensitivity estimation toward decision boundaries, with Hoeffding concentration guarantees on estimation accuracy.
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