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Si-Rui Han

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Preprint Aug 2026

Can Coding Agents Solve Repository-Level Issues with Rendered Code? An Exploratory Study of Visual Representations

This study evaluates rendered code in repository-level repair workflows and introduces controlled agent settings to separate unguided repository exploration from more structured repair stages, and positions rendered code as a viable but conditional compression mechanism for realistic coding agents.

Weijie Liang, Yuanfeng Song, Xing Chen et al. · 0 citations
#artificial intelligence Preprint Sep 2026

LexAgentHallu: A Hierarchical Benchmark for Profiling Hallucinations in Legal Agents

Evaluation across 18 proprietary and open-source agents uncovers a Right-Answer-Wrong-Reason effect and reveals that hallucination subclasses cluster rather than scatter, forming distinct agentic framework, legal task, and category profiles validate the diagnostic power of LexAgentHallu for evaluating agentic hallucina...

Yu-Jin Zhou, Min Zheng, Chuxue Cao et al. · 0 citations
#natural language process... Preprint Jan 2026

Towards Multi-modal Multi-turn Safety: From Agentic Interaction to Strategic Alignment

This work introduces MINT-Safe, an open-source visual multi-turn training dataset comprising 11,270 multi-image dialogues and 500 refusal VQA pairs, and proposes TAD-Align, a dialogue safety alignment framework centered on a turn-aware dual-objective reward function.

Han Zhu, Jia-Le Chen, Chengkun Cai et al. · 0 citations
Preprint Aug 2026

SkillProx: Self-Evolving Agent Skills via Proximal Textual Gradient Descent

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
Conference Open access 2026

Omni-RewardBench: Toward a Comprehensive Evaluation of Generative Reward Models Across Modalities

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. · 0 citations

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