arXiv:2510.11713v4 Announce Type: replace Abstract: Real-world applications of Large Reasoning Models (LRMs) often require reasoning about changing prompts or environments. In this work, we challenge the frozen world assumption and evaluate LRM robustness under two realistic dynamic scenarios: interruptions, which test the accuracy of model responses under budget-constrained outputs, and dynamic context, which tests model adaptation to in-flight changes. Across mathematics and programming benchmarks that require long-form reasoning, static evaluations consistently overestimate robustness: even
The paper challenges the 'frozen world assumption' of Large Reasoning Models, addressing a critical limitation as these models are deployed in dynamic real-world environments.
This research highlights significant accuracy and robustness issues in Large Reasoning Models when faced with interruptions and dynamic contexts, directly impacting their real-world applicability and reliability.
The understanding of LRM robustness shifts from static evaluation overestimation to a more realistic assessment of performance under dynamic conditions, demanding new development paradigms.
- · Companies developing more robust LRM architectures
- · Researchers focused on dynamic AI system design
- · Hardware providers enabling faster inference and context switching
- · Developers relying solely on static LRM evaluations
- · Applications requiring high-precision, real-time LRM responses in complex enviro
Current Large Reasoning Models may be significantly less reliable in real-world, dynamic applications than static benchmarks suggest.
New architectural designs and training methodologies will emerge to address the challenges of LRM interruptibility and dynamic context adaptation, potentially increasing model complexity and computational demands.
The development of truly autonomous AI agents will be constrained until robust solutions for dynamic reasoning and interruption handling in LRMs are widely achieved.
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