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Dingwei Zhu

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#artificial intelligence Preprint Sep 2026

Sci-MMR: Benchmarking Multi-Step Evidence-Grounded Scientific Reasoning in Multimodal Agents

Sci-MMR is introduced, a benchmark for multi-step evidence-grounded scientific reasoning built on structured argument graphs linking scientific claims, citation-grounded knowledge, visual evidence, and supporting regions, and it is found that current answer-centric benchmarks substantially overestimate the evidence-gro...

Jia-Qiang Li, Ya-Jie Yang, Zhi-Heng Xi et al. · 0 citations

Prefix-Adaptive Block Diffusion for Efficient Document Recognition

The Prefix-Adaptive Block Diffusion Model (PA-BDM) is proposed, which replaces intra-block bidirectional denoising with causal denoising from prefix to suffix and treats the block size as a maximum candidate range rather than a fixed commitment unit.

Ming-Xu Chai, Zi-Yu Shen, Chen-Yu Liu et al. · 0 citations

JFTA-Bench: Evaluate LLM's Ability of Tracking and Analyzing Malfunctions Using Fault Trees

A novel textual representation of fault trees is proposed, and a benchmark for multi-turn dialogue systems that emphasizes robust interaction in complex environments is constructed, evaluating a model's ability to assist in malfunction localization.

Yuhui Wang, Zhi-Xiong Yang, Ming Zhang et al. · 0 citations
Preprint Aug 2026

CAFE: Self-Improving Search Agents Need Co-Evolving Feedback

CAFE (Coupled Agent--Feedback Evolution), a framework in which a shared-parameter model alternates between search-agent and critic roles, is introduced, suggesting that a self-improving search agent needs feedback that co-evolves with the policy it guides.

Bo-Yang Liu, Sen-Jie Jin, Pei-Xin Wang et al. · 1 citation
Conference Open access Jul 2026

AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments

AgentGym2 is presented, a new evaluation framework with task instances grounded in real-world end-to-end working demands that measures agents'ability to execute end-to-end procedures, discover tools via exploration, compose tools for unseen tasks, and remain robust to noisy and underspecified information.

Zhiheng Xi, Dingwen Yang, Jiaqi Liu et al. · 1 citation

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