When Can LLM Reasoning Reliably Reuse Intermediate Results?
Sampling complete solutions gives large language models more chances to solve a problem, but each attempt starts again. For tasks with several subproblems, all required answers must be correct in the same attempt. Retaining intermediate results can avoid repeating correct work, but errors in those results may affect later attempts. We study when this reuse is reliable and how much computation it requires. For local retries that stop when no candidate passes within the retry budget, we give conditions for correct completion and bound the average number of attempts. We also show how checking cost and the chance of passing a check affect total computation. Experiments across eight reasoning ...
| Authors | Deyuan Liu, Zecheng Wang, Xuanyi Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 21 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.