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SkillCorpus is, to the authors' knowledge, the first end-to-end account of when a curated, retrieval-served community corpus improves real agent tasks, and where it does not, and is evaluated end-to-end across three benchmarks.
Large Language Models (LLMs) have enabled capable agents across diverse applications. Beyond the foundation model, the performance of an agent is governed by the surrounding agent harness, including prompts, tools, control loops, etc. Automatically evolving this harness offers a promising pathway to agent improvement,...
EvoAgentBench is introduced, a benchmark for agent self-evolution via Ability-guided transfer across four agentic domains: web research, algorithmic reasoning, software engineering, software engineering, and knowledge work, which shifts self-evolution evaluation from aggregate accuracy comparison to fine-grained diagno...
To the authors' knowledge, JIT-Agent is the first model purpose-built for just-in-time harness generation, establishing harness intelligence as a trainable, transferable, and compounding dimension of agent capability orthogonal to model scaling.
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