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cs.AI2026

ReDraft, Don't Just Distill: Reference-Driven Revision for Continual VLLM Post-Training

Zhihao Zhang, Mingqi Wu, Qiaole Dong +15

Continual post-training of large multimodal models should add new capabilities while preserving those from pre-training, and the two goals pull in opposite directions. SFT gives ex…

cs.AI2026

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

Jiaqiang Li, Yajie Yang, Zhiheng Xi +15

Autonomous research agents are increasingly expected to search the literature, analyze experimental evidence, and generate scientific hypotheses. These capabilities require multi-s…

cs.AI2026

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

Zhiheng Xi, Dingwen Yang, Jiaqi Liu +21

Language agents, i.e., LLM agents, progress rapidly and are increasingly deployed in production environments. This trend underscores the urgent need for rigorous and realistic eval…

cs.AI2026

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

Yuhui Wang, Zhixiong Yang, Ming Zhang +10

In the maintenance of complex systems, fault trees are used to locate problems and provide targeted solutions. To enable fault trees stored as images to be directly processed by la…

cs.AI2026

MagicAgent: Towards Generalized Agent Planning

Xuhui Ren, Shaokang Dong, Chen Yang +21

The evolution of Large Language Models (LLMs) from passive text processors to autonomous agents has established planning as a core component of modern intelligence. However, achiev…