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This work proposes When2Think, an RLVR-based post-training framework for instance-adaptive computation allocation that requires neither a learned reward model nor a learned critic, and offline reference caching avoids online reference-model queries during policy updates.
Q-TIE is proposed, a re-ranking framework based on learned Temporal Intent Extraction that generalizes beyond predefined rules via model-based learning while explicitly modeling temporal constraints as a separate signal - jointly achieving what each paradigm typically trades off.
Large Reasoning Models (LRMs) achieve strong performance on complex tasks but exhibit systematic inefficiency: they often overthink easy problems and underthink hard ones. Existing approaches based on uniform length penalties or rigid routing incur an efficiency tax, trading reduced computation on easy instances for ac...
Large language models are increasingly used as judges to measure social bias in text, yet the passages they judge are often noisy, containing typos, informal spelling, and broken punctuation. The consequences of such surface noise for social bias measurement remain unclear. To investigate this question, we apply five r...
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