9 papers
When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning
Yang Xiang, Yixin Ji, Ruotao Xu +4
Large reasoning models (LRMs) have achieved remarkable performance in complex reasoning tasks, driven by their powerful inference-time scaling capability. However, LRMs often suffe…
Not All Negative Samples Are Equal: LLMs Learn Better from Plausible Reasoning
Zixiang Di, Jinyi Han, Shuo Zhang +8
Learning from negative samples holds great promise for improving Large Language Model (LLM) reasoning capability, yet existing methods treat all incorrect responses as equally info…
From Atoms to Chains: Divergence-Guided Reasoning Curriculum for Unlabeled LLM Domain Adaptation
Yongqi Wang, Xiaofeng Ji, Jie Wang +6
Adapting Large Language Models (LLMs) to specialized domains without human-annotated data is a crucial yet formidable challenge. Widely adopted knowledge distillation methods often…
MMLongCite: A Benchmark for Evaluating Fidelity of Long-Context Vision-Language Models
Keyan Zhou, Zecheng Tang, Lingfeng Ming +8
The rapid advancement of large vision language models (LVLMs) has led to a significant expansion of their context windows. However, an extended context window does not guarantee th…
Logo-VGR: Visual Grounded Reasoning for Open-world Logo Recognition
Zichen Liang, Jingjing Fei, Jie Wang +6
Recent advances in multimodal large language models (MLLMs) have been primarily evaluated on general-purpose benchmarks, while their applications in domain-specific scenarios, such…
CURE: Critical-Token-Guided Re-Concatenation for Entropy-Collapse Prevention
Qingbin Li, Rongkun Xue, Jie Wang +8
Recent advances in Reinforcement Learning with Verified Reward (RLVR) have driven the emergence of more sophisticated cognitive behaviors in large language models (LLMs), thereby e…