When Should a Reasoning Model Stop? Budgeting Test-Time Compute Under Deadlines

A reasoning service can improve benchmark accuracy by spending more inference compute and still become less useful when users share a constrained accelerator pool. Extra branches consume cache, verification consumes time, and long generations delay unrelated requests. This white paper develops a deployment decision protocol for allocating reasoning compute under deadlines, monetary budgets and tenant constraints. It synthesizes test-time scaling and inference-serving research, including Certaindex, SART and probabilistic demand modeling. The proposed contribution is an operational contract connecting admission, continuation, stopping and accounting to correctness delivered before a deadline. A worked example illustrates the accounting; a reproducible evaluation protocol specifies the evidence needed to validate the design. No new model, implemented scheduler, benchmark release or measured performance improvement is claimed.

Artificial Intelligence Cloud Computing Software Engineering