TL;DR
TIME is a framework that enables language models to perform context-triggered explicit reasoning in dialogue, improving efficiency and responsiveness to temporal cues by learning a control policy for reasoning bursts.
Contribution
It introduces a novel behaviorally aligned approach that triggers explicit reasoning based on temporal context, reducing token usage and enhancing model responsiveness.
Findings
TIME improves reasoning benchmark scores across multiple scales.
Explicit reasoning tokens are reduced by roughly an order of magnitude.
Models become more compact and context-sensitive in their reasoning behavior.
Abstract
Reasoning-oriented language models typically expose explicit reasoning as a long, front-loaded chain of "thinking" tokens before the main output, either always enabled or externally toggled at inference time. Although this can help on arithmetic, coding, and other multi-step tasks, it is costly, weakens claim-level auditability, and does not allow the model to re-trigger explicit reasoning once presentation has begun. In dialogue, these limitations are compounded by weak sensitivity to temporal structure: unless time is explicitly stated in text, standard models treat replies separated by seconds and replies separated by weeks as equivalent. We introduce TIME (Temporally Intelligent Meta-reasoning Engine), a behavioral alignment framework that learns explicit reasoning as a context-triggered control policy rather than a fixed response mode. TIME augments dialogue with optional ISO 8601…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
