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cs.CL2026
Self-Evolving Coding Agents via Harness-Policy Optimization
Zhepei Wei, Wenlin Yao, Baolin Peng +5
Large language model (LLM) agents have shown promising potential in coding tasks, and their performance is jointly determined by the policy model and the harness that mediates thei…
cs.CL2026
The Tool Illusion: Rethinking Tool Use in Web Agents
Renze Lou, Baolin Peng, Wenlin Yao +5
As web agents rapidly evolve, an increasing body of work has moved beyond conventional atomic browser interactions and explored tool use as a higher-level action paradigm. Although…
cs.CL2026★ 1 cited
Reinforcement World Model Learning for LLM-based Agents
Xiao Yu, Baolin Peng, Ruize Xu +6
Large language models (LLMs) have achieved strong performance in language-centric tasks. However, in agentic settings, LLMs often struggle to anticipate action consequences and ada…