Large Language Models

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Large Language Models(Llms

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Reasoning emerges from constrained inference manifolds in large language models
May 2, 2026Yanbiao Ma, Fei Luo, Linfeng Zhang +10Large Language Model Reasoning Large Language Model Performance
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Apr 28, 2026Alexander W. Lee, Benjamin Han, Shayak Sen +3Noisy Claim Verification Verifier
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Apr 27, 2026Xinyan Zhu, Yaoqi Liu, Yue Gao +3Hallucination Detection Knowledge Graphs
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Apr 23, 2026Yuto Nishida, Naoki Shikoda, Yosuke Kishinami +4Memorization High Bigram Repetition Frequency
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Apr 23, 2026Xinyu Zhang, Boxuan Zhang, Yuchen Wan +5Benchmarking Large Language Models Large Language
Reasoning Primitives in Hybrid and Non-Hybrid LLMs: Do Architectural Differences Yield Advantages in State-Tracking and Recall?
Apr 23, 2026Shivam Rawat, Lucie Flek, Florian Mai +1Large Language Model Reasoning Search-Augmented Reasoning
Behavioral Consistency and Transparency Analysis on Large Language Model API Gateways
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May 2, 2026 · cs.LGJ/K move · Enter open · S save

Reasoning emerges from constrained inference manifolds in large language models

Yanbiao Ma, Fei Luo, Linfeng Zhang, Chuangxin Zhao+9

Renmin University of China · Tsinghua University · Xiaomi EV · Xiamen University

Reasoning in large language models is predominantly evaluated through labeled benchmarks, conflating task performance with the quality of internal inference. Here we study reasoning as an intrinsic dynamical process by examining the evolution of internal representations during inference. We find that inference-time dynamics consistently self-organize into low-dimensional manifolds embedded within high-dimensional representation spaces. we find that such geometric compression, although pervasive, is not sufficient for stable or reliable reasoning. Instead, effective reasoning dynamics emerge within a constrained structural regime characterized by three conditions: adequate representational expressivity, spontaneous manifold compression, and preservation of non-degenerate information volume within the compressed subspace. Models outside this regime exhibit characteristic pathological inference dynamics. Based on these insights, we introduce a unified, label-free diagnostic computed solely from internal dynamics. These findings suggest that reasoning in LLMs is fundamentally governed by geometric and informational constraints, offering a complementary framework to benchmark-centric assessment.

Large Language Model Reasoning Large Language Model Performance

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