4 papers
What Proves You Wrong: Benchmarking Language Models on Falsifiable Research Ideation
Ziyue Wang, Aomufei Yuan, Yiran Yao +10
Large language models are increasingly used to propose research ideas, yet the prevailing ways of judging such ideas supply no shared decision rule: free-form judging sways with st…
REVES: REvision and VErification--Augmented Training for Test-Time Scaling
Yuanxin Liu, Ruida Zhou, Xinyan Zhao +6
Test-time scaling via sequential revision has emerged as a powerful paradigm for enhancing Large Language Model (LLM) reasoning. However, standard post-training methods primarily o…
Thinking Without Images: Internalizing Visual Manipulation with On-Policy Self-Distillation
Yishuo Cai, Jiahui Liu, Yuanxin Liu +9
''Thinking with Images'' has emerged as an effective paradigm for fine-grained visual reasoning: by explicitly zooming into relevant regions and reasoning over crops, models can ac…
BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning
Han Zhong, Yutong Yin, Shenao Zhang +10
Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, yet generating reliable reasoning processes remains a significant challenge. We p…