Is Your Model Thinking or Just Stagnating? PUMA: Diagnosing Reasoning Pathology via Phase-Momentum Alignment
Test-time scaling empowers Large Reasoning Models (LRMs) to tackle complex tasks via extensive Chain-of-Thought (CoT). However, this often induces the "overthinking" paradox, where redundant reasoning increases computational overhead without guaranteeing accuracy. Existing test-time efficiency optimization methods primarily fall into two categories: information-theoretic approaches, which are prone to "deceptive convergence" where low uncertainty masks hallucinations, and latent representation analyses, which are often post-hoc, lacking the real-time sensitivity for dynamic reasoning. To bridg
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- PossiblePossibly related (embedding) · 50%New benchmark exposes reasoning gaps in top models →
- PossiblePossibly related (embedding) · 48%Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling →
- LinkedLinked via arxiv author · 85%Haocheng Yang →
“Is Your Model Thinking or Just Stagnating? PUMA: Diagnosing Reasoning Pathology via Phase-Momentum Alignment”
- LinkedLinked via arxiv author · 85%Guangyang Ye →
“Is Your Model Thinking or Just Stagnating? PUMA: Diagnosing Reasoning Pathology via Phase-Momentum Alignment”
- LinkedLinked via arxiv author · 85%Wuyang Zhang →
“Is Your Model Thinking or Just Stagnating? PUMA: Diagnosing Reasoning Pathology via Phase-Momentum Alignment”
- LinkedLinked via arxiv author · 85%Yifan Xu →
“Is Your Model Thinking or Just Stagnating? PUMA: Diagnosing Reasoning Pathology via Phase-Momentum Alignment”
- LinkedLinked via arxiv author · 85%Zhijun Fan →
“Is Your Model Thinking or Just Stagnating? PUMA: Diagnosing Reasoning Pathology via Phase-Momentum Alignment”
- LinkedLinked via arxiv author · 85%Xiang Xia →
“Is Your Model Thinking or Just Stagnating? PUMA: Diagnosing Reasoning Pathology via Phase-Momentum Alignment”