Visual modality has recently been explored as a way to compress textual tokens, including rendering code as images for static code understanding. We study whether this representation can serve as operational context for agentic coding, where an agent must navigate repositories, edit source files, and verify executable...
Weijie Liang, Yuanfeng Song, Xing Chen et al.· 0 citations
As Large Language Models (LLMs) evolve into autonomous agents that alter real-world states, ensuring operational safety across multi-step workflows has become a critical challenge. While recent work has moved beyond single-turn evaluation toward multi-turn paradigms, key limitations persist: step-level methods treat ac...
Jia-Peng Sun, Yu-Jin Zhou, Han Zhu et al.· 0 citations
As large language models are increasingly deployed as tool-augmented legal agents, they introduce agentic hallucinations where tool-call and reasoning errors cascade into fabricated holdings and miscited authority. However, existing legal benchmarks evaluate only single-turn QA with outcome-level metrics, while agentic...
Yu-Jin Zhou, Min Zheng, Chuxue Cao et al.· 0 citations
SkillProx is introduced, a proximal-gradient-inspired forward--backward framework that couples closed-loop diagnostic evolution with utility-aware proximal refinement and demonstrates the complementary effects of closed-loop diagnosis and proximal refinement.
Mingxuan Zheng, Yu-Jin Zhou, Chuxue Cao et al.· 2 citations
Omni-RewardBench is introduced, the first benchmark for comprehensive evaluation of ORMs across modalities and demonstrates that current OLLMs fall short as reward models, revealing several common failure modes such as perception failure, modality dominance failure, and cross-modal fusion failure.
Chi-Min Chan, Yujin Zhou, Pengcheng Wen et al.· Annual Meeting of the Associ...· 0 citations
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