Beyond Depth and Width: The Information-Slack Dilemma in Streaming Test-Time Compute

Abstract:The same task and compute budget can require different reasoning policies when evidence arrives in a different order. Early computation has more time to finish but rests on incomplete or revisable evidence; waiting improves information while shrinking computational slack. We call this the information-slack dilemma. We take the evidence-dependent computational job as the unit of analysis: when to start it, what supports its result, and when that result can be committed. Advance computation is valuable only insofar as its benefits survive the costs of verification, invalidation, and recovery. This applies to grounded incremental processing and reusable preparation as well as future-dependent speculation. We propose a research agenda on computation under evolving evidence, prioritizing selective recovery under controlled evidence revisions. Evaluation should separate earlier-execution effects, deployment value against a full-input alternative, and the added value of predictive policies, while accounting for shared-resource costs. The objective is not maximal advance computation, but more trustworthy, on-time responses within a declared resource envelope.

Position paper. 10 pages, 1 figure, 3 tables

Computation and Language (cs.CL)

arXiv:2609.14995 [cs.CL]

(or arXiv:2609.14995v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.14995 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.