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cs.AI2026
Targeted Retrieval, Compact Representations: How CoT Reasoning Improves Long-Context Counting
Liang Twist Shan, Tianyu Hu, Hao Yan +1
Large language models (LLMs) have been rapidly improving in long-context tasks, powered by Chain-of-Thought (CoT) reasoning. However, the internal mechanisms underlying this improv…
cs.AI2026
DocSeeker: Structured Visual Reasoning with Evidence Grounding for Long Document Understanding
Hao Yan, Yuliang Liu, Xingchen Liu +5
Existing Multimodal Large Language Models (MLLMs) suffer from significant performance degradation on the long document understanding task as document length increases. This stems f…
cs.LG2026
Task Vector Geometry Underlies Dual Modes of Task Inference in Transformers
Hao Yan, Haolin Yang, Yiqiao Zhong
Transformers are effective at inferring the latent task from context via two inference modes: recognizing a task seen during training, and adapting to a novel one. Recent interpret…