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

1 citations · 1 across the 9 of their papers we have counts for

collaborators

11 papers

cs.CL2026

Offline Guidance, Online Reasoning: Reusing LLM Feedback for Small Language Models

Bohan Zhang, Linan Yue, Weibo Gao +3

Large language models (LLMs) offer strong reasoning capabilities but are often costly to access through commercial APIs, while small language models (SLMs) are easier to deploy loc…

cs.CL2026

UniRRM: Unified Reasoning Reward Models Across Languages and Evaluation Paradigms

Peng Lai, Yichao Du, Junchao Wu +6

Reinforcement learning (RL) excels on tasks with verifiable rewards, but in open-ended tasks, the reliability of reward models remains a key challenge. Existing solutions either de…

cs.CV2026

AI-generated Images Challenge Visual Trust in High-risk Scenarios

Yi-Zhi Wang, Yichen Xiao, Linan Yue +5

Rapid advances in image generation are eroding the evidentiary value of visual content in settings where authenticity can affect public safety and personal reputation. Yet existing…

cs.CV2026

ViCoStream: Streaming VideoLLMs Can Run Beyond 100 FPS with Stage-Wise Coordinated Inference

Yang Tan, Junlong Tong, Linan Yue +3

Streaming VideoLLMs must continuously process incoming video while maintaining low query latency, making both video-ingestion throughput and query-time responsiveness critical for…

cs.AI2026★ 1 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

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.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…

cs.AI2025

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.CV2025

Agentic Video Intelligence: A Flexible Framework for Advanced Video Exploration and Understanding

Hong Gao, Yiming Bao, Xuezhen Tu +6

Video understanding requires not only visual recognition but also complex reasoning. While Vision-Language Models (VLMs) demonstrate impressive capabilities, they typically process…

cs.SE2025

Code2MCP: Transforming Code Repositories into MCP Services

Chaoqian Ouyang, Ling Yue, Shimin Di +5

The Model Context Protocol (MCP) aims to create a standard for how Large Language Models use tools. However, most current research focuses on selecting tools from an existing pool.…