Hugging Face Trending Papers
When Reasoning Goes Astray: Attention Dynamics of Uncontrolled Reasoning
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arXiv AI
3d ago
When Reasoning Goes Astray: Attention Dynamics of Uncontrolled Reasoning
The paper introduces RADAR, a method that models large reasoning models (LRMs) as four states and uses dynamic attention responses to detect uncontrolled reasoning in real time. RADAR identifies abnormal attention patterns that precede repetitive loops, and the authors demonstrate that realigning these patterns reduces looping while maintaining performance. The study offers a mechanistic explanation of how benign reasoning can degenerate into harmful behavior and provides actionable guidance for runtime interventions.
arXiv AI
Jun 3
Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models
arXiv:2606. 02835v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) improve performance by generating explicit intermediate reasoning traces through increased test-time compute, yet the assumption that longer reasoning is consistently beneficial remains under-examined.
arXiv AI
Sep 3
When Can Large Reasoning Models Save Thinking? Mechanistic Analysis of Behavioral Divergence in Reasoning
The paper investigates why large reasoning models (LRMs) often continue to think even when prompted to stop, a phenomenon called "Still-thinking". By examining confidence at the thinking-termination boundary, internal attention divergences, and attention allocation across prompt segments, the authors find that high perplexity and greater attention to the original question correlate with continued thinking. They propose an attention‑intervention method that suppresses explicit reasoning, which reduces inefficiency but also lowers accuracy, underscoring a trade‑off between instruction compliance, inference speed, and correctness.
arXiv AI
Jul 21
Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering
arXiv:2607. 18100v1 Announce Type: new Abstract: Extended reasoning has become standard for frontier Large Language Models (LLMs), yet the trajectories these models produce remain largely uncontrollable.
arXiv AI
Sep 1
Stop Before You Fail: Operational Capability Boundaries for Mitigating Unproductive Reasoning in Large Reasoning Models
arXiv:2509.24711v4 Announce Type: replace Abstract: Current answering paradigms for Large Reasoning Models (LRMs) often fail to account for the fact that some questions may lie beyond the model's ope...
arXiv Machine Learning
Jun 2
Are Large Reasoning Models Interruptible?
arXiv:2510. 11713v4 Announce Type: replace-cross Abstract: Real-world applications of Large Reasoning Models (LRMs) often require reasoning about changing prompts or environments.