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pith:YMYHA3CW

pith:2026:YMYHA3CWK2DQOAODPCE7YEVEXU

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Conformal Thinking: Risk Control for Reasoning on a Compute Budget

Alvin Zhang, Anushri Suresh, Benjamin Van Durme, Daniel Khashabi, Eric Nalisnick, Mehrdad Farajtabar, Rishi More, William Jurayj, Xi Wang

Distribution-free risk control sets upper and lower thresholds so LLMs stop reasoning early while keeping error rates below a user target.

arxiv:2602.03814 v2 · 2026-02-03 · cs.AI · cs.LG

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Claims

C1strongest claim

Given a target risk and a validation set, we use distribution-free risk control to optimally specify these stopping mechanisms. For scenarios with multiple budget controlling criteria, we incorporate an efficiency loss to select the most computationally efficient exiting mechanism.

C2weakest assumption

The validation set is representative of future test instances so that the distribution-free risk guarantees transfer; the parametric form chosen for the lower threshold is flexible enough to capture unsolvable cases without excessive premature stopping.

C3one line summary

Conformal risk control with upper and lower thresholds lets LLMs adaptively stop reasoning while guaranteeing a maximum error rate and minimizing token use.

References

13 extracted · 13 resolved · 3 Pith anchors

[1] DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning · arXiv:2501.12948

[2] Efficiently serving llm reasoning programs with certaindex

[3] URL https: //aclanthology.org/2025.acl-short.50/. Langley, P. Crafting papers on machine learning. In Langley, P. (ed.),Proceedings of the 17th International Conference on Machine Learning (ICML 2000) 2025

[4] Fractured chain-of-thought reasoning

[5] Xin Liu and Lu Wang

Formal links

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Cited by

1 paper in Pith

Process Supervision of Confidence Margin for Calibrated LLM Reasoning

Receipt and verification

First computed	2026-05-17T23:39:16.387425Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c330706c5656870701c37889fc12a4bd0868230754bbb8c914059045df08a62f

Aliases

arxiv: 2602.03814 · arxiv_version: 2602.03814v2 · doi: 10.48550/arxiv.2602.03814 · pith_short_12: YMYHA3CWK2DQ · pith_short_16: YMYHA3CWK2DQOAOD · pith_short_8: YMYHA3CW

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/YMYHA3CWK2DQOAODPCE7YEVEXU \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: c330706c5656870701c37889fc12a4bd0868230754bbb8c914059045df08a62f

Canonical record JSON

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    "abstract_canon_sha256": "cd7154bedad552d3beed7a7b2a5484f19224d573dfc42b98b25f01fbfdb32861",
    "cross_cats_sorted": [
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-02-03T18:17:22Z",
    "title_canon_sha256": "16717738a51e68b3ebc20dad8f8faae4f407ad3a18fc25924af456fae7e151cd"
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