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Paper Citation Record · LEDGER

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

As of 19 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 2026-08-18T06:34:40.430872+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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no resolver link, observed 2026-08-07T04:24:01.152545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:01.152545Z digest=sha256:bd3750085b6759747ef06406fa6d9f8aebbcf0037aaf90e2d80a26abbfeb504f

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

Resolution
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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=sha256:924a693d732aac4f7f5b49dd6863f66d553ee698c1ff9431b24f602f83bfe018

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=sha256:7c11e40afb8cc21248efb78b8632e95a195ec568f2e67f843bcc837303b59bd0

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T04:24:01.384046Z digest=sha256:de076e6d458271aa8e332f59c7fe3744d1a992e887ff91f576b84ba67127f439

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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no resolver link, observed 2026-08-07T04:24:01.461204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:01.461204Z digest=sha256:d62e24a114a9d1446747ceb770d67b2d366181fdf81bbf6f337f2fb44177cec4

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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no resolver link, observed 2026-08-07T04:24:01.508410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:01.508410Z digest=sha256:0936f1cc79ec5b9c094ad20795064f9e4321f57f628c9fcb7f22dd2214890dc5

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

Resolution
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no resolver link, observed 2026-08-07T04:24:01.619558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:01.619558Z digest=sha256:4c435a7b46f5f68e8cd7e62eb411513ee44685472e5541863b25fcadc5c74617

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-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:01.705143Z digest=sha256:84807d9cef48b96dcf222829996b4f816e25f0a6ff89c64833c4524ae6ae7e4f

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T04:24:01.765995Z digest=sha256:beefc99f7cfaad8283ba3f5c9a0c11bdf2a1aa0f1e8ca4f29c5cd6091338b319

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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no resolver link, observed 2026-08-07T04:24:01.871204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:01.871204Z digest=sha256:3e06f61b35056822394fca8e2a7adb73f5207663f30e8ac9bd545329dc0e3679

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

Resolution
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raw_fallback, observed 2026-08-07T04:24:05.069974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T04:24:01.964299Z digest=sha256:eb52512262ad5b2537f82105b69ea5bea963e67198c6063967fb37ecf9e20c1f

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T04:24:02.027730Z digest=sha256:788099fd4f4c45892e93af6414a5c084487b37e1bd65312dcdb3260b08e69804

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

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:02.102954Z digest=sha256:4516d3a68b64d89195db69702454ed68c6478cea07210369d7a310c655ef4e40

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:02.191167Z digest=sha256:ed6d32637cff091021d21aba76917f4557c2a0e6faff0499a6b1d9a0a48c1247

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

Resolution
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no resolver link, observed 2026-08-07T04:24:02.269001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:02.269001Z digest=sha256:16b60b09cda69103803eafd7b89df634a90535fd0c223420e13e59ce86ce6f71

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=sha256:05b2bde2f0b976533fd29ac70b43c666c0b39b0da6d3253c3a7e2e969046c8c7

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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no resolver link, observed 2026-08-07T04:24:02.421271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:02.421271Z digest=sha256:294beed046f0d6c13047dc2c5ab12ca9d497c5f49c9e026d67bab8eb7e46b41c

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
unresolved
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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T04:24:02.489694Z digest=sha256:57d41ae55eb5d49492d5c511032f42e7487b279337d24daaaec5074e267360e8

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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no resolver link, observed 2026-08-07T04:24:02.545702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:02.545702Z digest=sha256:d709734b62c31a26aae23818c6cd144edcd6830c886cdc1f14dc03f35e74185a

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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no resolver link, observed 2026-08-07T04:24:02.607794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:02.607794Z digest=sha256:b04f906e5348f320ae56ddcd3316d1d444a4579ff865698ceb83a4d74b10db0e

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:02.680283Z digest=sha256:47e8f53d4b8ed2fb61f13dc46d5cbaaf6d43254ec1a47584f8847a0cd618597c

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:02.731062Z digest=sha256:1271082f40cf20efc1369c8d4c84fac18d46ba88086c01755e1bb9ce6d78ff28

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T04:24:02.783764Z digest=sha256:e012f495a15508b92c58aa7a1d5db10120400988a4d752d3063abee7ec4144fd

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

Resolution
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no resolver link, observed 2026-08-07T04:24:02.825216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:02.825216Z digest=sha256:c26d8bf53a084b8a9d2ebae3aa274ba9b7a4f029534f94d67217f64c2b45e659

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T04:24:02.858228Z digest=sha256:63dfa920bc6aa21ed7b52aff5e09bdda5aeef1049c325b42be6031eb9b720982

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:02.927593Z digest=sha256:02e9dcbd956b619f30e1292acc2fbcf89444240b90214031896196d1229780cf

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

Resolution
unresolved
no resolver link, observed 2026-08-07T04:24:02.970521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:02.970521Z digest=sha256:a0afdbef03799861e9268e6e4a861ef6a292981695b27c579523b614632da7dc

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=sha256:0627aa4abbf4f4fc0ac9900bc149b3c7c39cefde8053bd5a90595e81e546e240

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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unresolved
no resolver link, observed 2026-08-07T04:24:03.079588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:03.079588Z digest=sha256:b01f14080bec80eef70a4a56d09ed3bed81034ce6a176d6d03c71050746b130a

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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no resolver link, observed 2026-08-07T04:24:03.115259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:03.115259Z digest=sha256:6918b0ba1e09e4683bdb0b4059508176c0445014faa46ac7e4c33de4d494d83e

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=sha256:36d96ee8730f119827142750d338d8ebd0448e45418b2c0439d5442f7e83aab4

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

Resolution
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=sha256:66e766b7043c15027c89aaed3ce7fbcf10bef652f99d0c306094c8ad024df3c7

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T04:24:03.264778Z digest=sha256:9a37e937e4da88a2be49306a5cb45f1628badedab0f813d7f965199384549072

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

Resolution
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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T04:24:03.306237Z digest=sha256:1c229c827ee105f2c911e1abee2ce7d219ecdbb5aceabdf7a6db37647a087481

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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no resolver link, observed 2026-08-07T04:24:03.355899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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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

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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

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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This paper cites online" 'onlinestring :=.

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

Reference 38

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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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.

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

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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