3 papers
cs.CL2026
On the Step Length Confounding in LLM Reasoning Data Selection
Bing Wang, Rui Miao, Chen Shen +7
Large reasoning models have recently demonstrated strong performance on complex tasks that require long chain-of-thought reasoning, through supervised fine-tuning on large-scale an…
cs.AI2026
Reasoning Fails Where Step Flow Breaks
Xiaoyu Xu, Yulan Pan, Xiaosong Yuan +4
Large reasoning models (LRMs) that generate long chains of thought now perform well on multi-step math, science, and coding tasks. However, their behavior is still unstable and har…
cs.CL2026
ART: Attention Replacement Technique to Improve Factuality in LLMs
Ziqin Luo, Yihao Quan, Xiaofeng Zhang +2
Hallucination in large language models (LLMs) continues to be a significant issue, particularly in tasks like question answering, where models often generate plausible yet incorrec…