most citedWhen AI reviews science: Can we trust the referee?

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cs.AI2026

Towards Safer Large Reasoning Models by Promoting Safety Decision-Making before Chain-of-Thought Generation

Jianan Chen, Zhifang Zhang, Shuo He +3

Large reasoning models (LRMs) achieved remarkable performance via chain-of-thought (CoT), but recent studies showed that such enhanced reasoning capabilities are at the expense of…

cs.AI20261 cited

When AI reviews science: Can we trust the referee?

Jialiang Wang, Yuchen Liu, Hang Xu +7

The volume of scientific submissions continues to climb, outpacing the capacity of qualified human referees and stretching editorial timelines. At the same time, modern large langu…

cs.AI2026

Training Multimodal Large Reasoning Models Needs Better Thoughts: A Three-Stage Framework for Long Chain-of-Thought Synthesis and Selection

Yizhi Wang, Linan Yue, Min-Ling Zhang

Large Reasoning Models (LRMs) have demonstrated remarkable performance on complex reasoning tasks through long Chain-of-Thought (CoT) reasoning. Extending these successes to multim…

cs.AI2026

Bridging Efficiency and Transparency: Explainable CoT Compression in Multimodal Large Reasoning Models

Yizhi Wang, Linan Yue, Min-Ling Zhang

Long chains of thought (Long CoTs) are widely employed in multimodal reasoning models to tackle complex tasks by capturing detailed visual information. However, these Long CoTs are…

cs.AI2026

Guided by Trajectories: Repairing and Rewarding Tool-Use Trajectories for Tool-Integrated Reasoning

Siyu Gong, Linan Yue, Weibo Gao +4

Tool-Integrated Reasoning (TIR) enables large language models (LLMs) to solve complex tasks by interacting with external tools, yet existing approaches depend on high-quality synth…