Reasoning substantially improves language models' ability to solve difficult problems, but long reasoning traces make these gains expensive. Efficient reasoning should express useful analysis in fewer
Rewriting reasoning histories into compact state records reduces future reasoning length and token costs in open-weight models without retraining.
Reasoning substantially improves language models' ability to solve difficult problems, but long reasoning traces make these gains expensive. Efficient reasoning should express useful analysis in fewer tokens by reducing repetition and text that adds little value to solving the problem. Progress in proprietary models motivates this goal, yet their hidden scratchpads limit direct study of how it is achieved. We investigate whether open-weight models can generate less verbose reasoning by changing the form of their own earlier thoughts, without retraining. Our intervention rewrites prior reasoning into compact state records before pending tool results are revealed, then compares continuations…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Why Pay for Long Reasoning When Rewrite Do Trick? Causal Effects of Reasoning History Compaction on Future Generation and Agent Cost was first seen on Hugging Face · Daily papers on Nov 6, 2026. Since then it has appeared in 24 checks on 3 different days. Its best position was #18 of 30, reached on Nov 8, 13:28 UTC. It is #19 right now, holding steady at #19 over the last 6 checks.
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Yes. As of Nov 8, 17:28 UTC it is #19, holding steady at #19 over the last 6 checks.
#18 on Hugging Face · Daily papers, on Nov 8, 13:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 6, 2026 and has been on the list on 3 days (24 checks) since.