AI RESEARCH
Are Large Reasoning Models Interruptible?
arXiv CS.LG
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ArXi:2510.11713v4 Announce Type: replace-cross 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.