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Paper Citation Record · LEDGER

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization

As of 13 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 3 inbound Pith citation observations for arXiv:2506.10822.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.10822 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:24:03.563539Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:54:16.766414Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T09:19:43.891734Z

Reference resolution

39 of 39 outbound references displayed

  • verified exact0
  • verified fuzzy3
  • unresolved36
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5404fa91-5fff-4202-b533-2710c7b4e10c · outbound

This paper cites L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:01.152545Z digest=sha256:4a548730dae8ea3de87ee8277dbc35c1a971232ff12daf7afc1800ae8153bb6f

Observation 34ea02ed-bc12-4adf-bf32-fb438273c6a9 · outbound

This paper cites Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models

Reference 2

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no resolver link, observed 2026-08-07T04:24:01.236638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:01.236638Z digest=sha256:50ede3f5d296163362f06c75743ce00b8b6f4f38336636022c90a8fa3f4fe22a

Observation 4fe7ae4c-3a13-42eb-a79e-3a7470f2ebdc · outbound

This paper cites Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 3

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no resolver link, observed 2026-08-07T04:24:01.308511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:01.308511Z digest=sha256:3b3ed1bf21df1e2fb0be72eebb92cb95e16288f7b12b6b82cc904b149aac8727

Observation 2623af2b-e124-468e-b761-ea109da9f813 · outbound

This paper cites an unresolved cited work.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Unresolved cited work

Reference 4

Resolution
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raw_fallback, observed 2026-08-07T04:24:05.446844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-07T04:24:01.384046Z digest=sha256:898ad3e8e02344acfa485b1b174adf0dc067847123e92d2b17ab592e7d98bc31

Observation f51e2e38-bdd0-4d4a-a857-58ff832411a1 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Training Verifiers to Solve Math Word Problems

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:01.461204Z digest=sha256:b92f452ebe12338239da5325878c883df0555a0d70989b7241b8d5b88705d131

Observation 98814e70-03e1-42da-824a-9817c40d10ef · outbound

This paper cites The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:01.508410Z digest=sha256:f7b53752bc4811b893a28300c64bad6f96bf5ddb5161d593ff4eda6caf1e8e59

Observation c975d91d-02ee-43e8-86ac-857102901f45 · outbound

This paper cites Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:01.619558Z digest=sha256:4a11998cc58c4e5a6088f25600812bb8e6a24b56e01339698e3b71bb5a3b6341

Observation 43213829-d65c-4a63-b610-80379d7ad101 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 8

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source=arxiv_source observed=2026-08-07T04:24:01.705143Z digest=sha256:410aa1b6d259f601b52ae289eeb017f6d9ee5aa821450928c645d3436c9e49a1

Observation 39b38077-a240-4858-a691-dd927ce51381 · outbound

This paper cites an unresolved cited work.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Unresolved cited work

Reference 9

Resolution
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raw_fallback, observed 2026-08-07T04:24:05.256164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-07T04:24:01.765995Z digest=sha256:0dd6f1a3d5f901c279b229b413c64d58aff8f502744bad7de68ab05b3e94ead9

Observation f011c757-b625-41f1-bb60-b830d868ee44 · outbound

This paper cites The Llama 3 Herd of Models.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization The Llama 3 Herd of Models

Reference 10

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source=arxiv_source observed=2026-08-07T04:24:01.871204Z digest=sha256:e0831d939a9bddc708e3384b8c7f0bee30fc9f869f8498be1a98739f0326ab7b

Observation fe7e531b-9f6c-454b-b2ce-0d280933f93d · outbound

This paper cites an unresolved cited work.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Unresolved cited work

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-07T04:24:01.964299Z digest=sha256:c599ce3f893459b073ddc2885652e128bd8a5fd9ffaa3bf0eb0303e834a2db65

Observation 5501d904-6679-4e88-8817-20592503c0dd · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen - Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen - Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:24:04.919480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-07T04:24:02.027730Z digest=sha256:d6b51b120509766d209d12a0ae99a51c92a8b2bab84917b6d8223184fbfd9cf4

Observation 3750dc38-2bfd-4a39-a36a-020960dc4387 · outbound

This paper cites The Impact of Reasoning Step Length on Large Language Models.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization The Impact of Reasoning Step Length on Large Language Models

Reference 13

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T04:24:02.102954Z digest=sha256:37f627b02f177f9ca056dc8c50c414aef72f1e51449fb3056d601cb716506887

Observation 6bf73b08-5a11-4830-8323-5dd4b1e1cdb6 · outbound

This paper cites an unresolved cited work.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Unresolved cited work

Reference 14

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no resolver link, observed 2026-08-07T04:24:02.191167Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:02.191167Z digest=sha256:019412ce199a3159bde727251c8bf8c4fa54b2bb81d1844dffd5e6700bd54b9b

Observation ba57bb8a-0c5c-4241-93b9-14de7bab1e93 · outbound

This paper cites How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach

Reference 15

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source=arxiv_source observed=2026-08-07T04:24:02.269001Z digest=sha256:3cb31c90285b5497897ed592fef0f6468521fde417b8bb9704b6862959d19730

Observation 314ff115-b3cb-4b0a-ba0e-2c1b8acb06ca · outbound

This paper cites Search-o1: Agentic Search-Enhanced Large Reasoning Models.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Search-o1: Agentic Search-Enhanced Large Reasoning Models

Reference 16

Resolution
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no resolver link, observed 2026-08-07T04:24:02.371341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:02.371341Z digest=sha256:9e1e8b89c974210a22a8a8239e582bafa52798547d7740aaa4874d760413253b

Observation 4c055b56-2701-4ae8-8fbb-2fc9eba799b1 · outbound

This paper cites Statistical Rejection Sampling Improves Preference Optimization.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Statistical Rejection Sampling Improves Preference Optimization

Reference 17

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source=arxiv_source observed=2026-08-07T04:24:02.421271Z digest=sha256:daa43c4289276d51288e8ac4823233435c1fbb72448b13331838e2a7aa936e17

Observation fb5cb2b4-fc13-40e1-a1e6-e3e33cae6a07 · outbound

This paper cites an unresolved cited work.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Unresolved cited work

Reference 18

Resolution
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raw_fallback, observed 2026-08-07T04:24:04.732686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-07T04:24:02.489694Z digest=sha256:52ade4491dc8fcceb2f24cbd4cdb0391fcb29d845907bacabc2cb1d43d2c3cb7

Observation ffdbcf01-12bc-429c-9fd0-0d23f918e9c8 · outbound

This paper cites An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning

Reference 19

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source=arxiv_source observed=2026-08-07T04:24:02.545702Z digest=sha256:ca60e6ea3b0f4f98ec9cd2466a6b4a8e5b957551d1db868e27dad9bf70f1676b

Observation aed5cf32-2f4c-4e9c-a25a-9e81c25584a5 · outbound

This paper cites Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems

Reference 20

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source=arxiv_source observed=2026-08-07T04:24:02.607794Z digest=sha256:e18d03e974694f43856def6f92842d515c8f1e9ca9405c5c7483344d12226cd4

Observation fee36d18-fa3d-4800-97c7-7a84c00bcc8f · outbound

This paper cites s1: Simple test-time scaling.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization s1: Simple test-time scaling

Reference 21

Resolution
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no resolver link, observed 2026-08-07T04:24:02.680283Z

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source=arxiv_source observed=2026-08-07T04:24:02.680283Z digest=sha256:5167a7f642a0bf1ab2348320aaf920934ec827653b6b49c4d1e5e1277b291d84

Observation 9a670ecf-dc91-42d6-865e-3d62d972189e · outbound

This paper cites GPT-4 Technical Report.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization GPT-4 Technical Report

Reference 22

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no resolver link, observed 2026-08-07T04:24:02.731062Z

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source=arxiv_source observed=2026-08-07T04:24:02.731062Z digest=sha256:caa7d7f8635b3cd6c010987f62ec208abd081b5815aa177c4cccd64adb26e153

Observation 178eb223-ce63-4f13-8bf7-1b01694aeee0 · outbound

This paper cites Manning, Stefano Ermon, and Chelsea Finn.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Manning, Stefano Ermon, and Chelsea Finn

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:24:04.573506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-07T04:24:02.783764Z digest=sha256:b24bc48a5a54b9ca79e1e937c7c5568df269f4b4c9baace9fbacfc49e80d3024

Observation d5857fad-8f67-4e61-8da7-386a809794a3 · outbound

This paper cites an unresolved cited work.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Unresolved cited work

Reference 24

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source=arxiv_source observed=2026-08-07T04:24:02.825216Z digest=sha256:c6dd26614df01aa3eb07362dc25ebd0be92fac32cb23e18aebd7a4b2c7fb68b3

Observation 3a0e35a4-9244-4757-95a2-62b8940ee970 · outbound

This paper cites an unresolved cited work.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Unresolved cited work

Reference 25

Resolution
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raw_fallback, observed 2026-08-07T04:24:04.392950Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-07T04:24:02.858228Z digest=sha256:022071b6834b2038c598b1f58f4f0fc65cfc545ed6c38399f9df78faece1eb1d

Observation 6ea0fa13-aade-43d9-9070-26c983e3a6ae · outbound

This paper cites Proximal Policy Optimization Algorithms.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Proximal Policy Optimization Algorithms

Reference 26

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no resolver link, observed 2026-08-07T04:24:02.927593Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:02.927593Z digest=sha256:4e729bf8f5245cd69594726e79299afe73a93fc2cbb2bc168ea54bda07031787

Observation c616a405-5f18-42e8-8f10-c5c04c36e468 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 27

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:02.970521Z digest=sha256:da19ac5496a18a31de2ea2ab44a6e16221aa1b4327078372a0e4cdda11ce42b1

Observation 6048d337-99a9-4d2e-8024-d4ff69da1082 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 28

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no resolver link, observed 2026-08-07T04:24:03.025228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:03.025228Z digest=sha256:22406e51573e5c2c715b1a39eef3bc0ac6d47b8d52eafc6d6976c1572a5c19d8

Observation e6d33c28-cd8c-4a02-b06a-252d6ae4fe64 · outbound

This paper cites Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

Reference 29

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source=arxiv_source observed=2026-08-07T04:24:03.079588Z digest=sha256:187137721f16b536ad3d509ae394d6b319f7433a27188ad55fea2faf1236a951

Observation 5b20b30a-4686-4524-8fa6-7861393d7cf2 · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 30

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:03.115259Z digest=sha256:ad64c8497a2c62cc36f5a0e249f0c8e25138aed44705f40b76b747bfe2b331c9

Observation f8b9a322-79dc-4824-92e7-a0242ed02690 · outbound

This paper cites an unresolved cited work.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Unresolved cited work

Reference 31

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no resolver link, observed 2026-08-07T04:24:03.180660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:03.180660Z digest=sha256:969c65260828f9ff9e25fcaa3436c0d8775fa20780944e046501cdb063339df7

Observation e50c0efe-b777-4e95-8282-549b71725a2d · outbound

This paper cites Stepwise Informativeness Search for Efficient and Effective LLM Reasoning.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Stepwise Informativeness Search for Efficient and Effective LLM Reasoning

Reference 32

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unresolved
no resolver link, observed 2026-08-07T04:24:03.223176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:03.223176Z digest=sha256:667483969a4f264a8cbb9bc92785e6851e0532215579be6757eb7e786769c8c1

Observation 4f9daa93-d461-4eae-a47f-79353d10285d · outbound

This paper cites Chi, Quoc V.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Chi, Quoc V

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:24:04.285467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-07T04:24:03.264778Z digest=sha256:b57851f43e2a9645a7b4e43689a8145207d556aba09ff19dfbc7153141ed7545

Observation a036fb45-bfb3-444b-a741-e8a060b5fdc6 · outbound

This paper cites an unresolved cited work.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Unresolved cited work

Reference 34

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unresolved
raw_fallback, observed 2026-08-07T04:24:04.187564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-07T04:24:03.306237Z digest=sha256:3c27d1fbbf1ca3f2533e166997f70140f15c29d63a6e9521ca222e77b6f5afb5

Observation 4f56cec3-8647-4a4b-971d-23051d6b58d5 · outbound

This paper cites Chain of Draft: Thinking Faster by Writing Less.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Chain of Draft: Thinking Faster by Writing Less

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation fe912320-4d28-4a1a-aeb6-06ddf9e965bd · outbound

This paper cites Qwen2.5 Technical Report.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Qwen2.5 Technical Report

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-07T04:24:03.420787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation c1c8df38-0e13-4ce2-bede-c837bc669fd3 · outbound

This paper cites an unresolved cited work.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization Unresolved cited work

Reference 37

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unresolved
no resolver link, observed 2026-08-07T04:24:03.469910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation bb3dafcc-d682-44ba-874c-a37b49243a37 · outbound

This paper cites online" 'onlinestring :=.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization online" 'onlinestring :=

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T04:24:03.512395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ab50e0db-9787-4f4f-b8b1-3c25ef1fbb95 · outbound

This paper cites write newline.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization write newline

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T04:24:03.563539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:03.563539Z digest=sha256:11d65b89f92c8c42daa56195f6701ca8c99055f26637195eafe3fa0adb80bc36

Pith citing papers

Observation c6533a2e-66b8-495e-a0f5-78258de6cca8 · inbound

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey cites this paper.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization

Reference 89

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unresolved
no resolver link, observed 2026-08-06T17:54:16.766414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 72452bd8-cde6-42b1-b99a-3d82cf6d34c6 · inbound

Beyond Penalizing Mistakes: Stabilizing Efficiency Training in Large Reasoning Models via Adaptive Correct-Only Rewards cites this paper.

Beyond Penalizing Mistakes: Stabilizing Efficiency Training in Large Reasoning Models via Adaptive Correct-Only Rewards ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization

Reference 25

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verified exact
arxiv_id, observed 2026-07-04T09:19:43.893126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation d58fab11-0a67-4879-9243-c240d0895db4 · inbound

Contrastive On-Policy Distillation cites this paper.

Contrastive On-Policy Distillation ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-01T13:40:39.777071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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