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.
Yanze Wang, Pengfei Yao, Tian-Yi Sun et al.· arXiv.org· 1 citation
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,...
Xiaotian Luo, Dizhan Xue, Fengxingyu Wang et al.· 4 citations
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...
DASH maps the gap between each local distillation signal and the sequence-level mean to an adaptive propagation gate and then uses these gates to control backward multi-step aggregation and improves over matched vanilla OPSD reruns on every benchmark at all three model scales.
Zhi-Yan Hou, Xinyu Tang, Hongyan An et al.· 3 citations
Experiments on multiple representative video benchmarks show that CRAFT consistently outperforms prior state-of-the-art token-compression methods and shows significant efficiency improvement.
Yu Chen, Xiao-Hong Li, Xiaole Wang et al.· 0 citations
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.
Gui-Bin Zhang, Leo Lu, Fang-Zhou Xie et al.· 14 citations
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