Paper Citation Record · LEDGER

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model

As of 7 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 5 inbound Pith citation observations for arXiv:2506.23840.

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

pith.paper-citation-record.v1
2506.23840 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:45:11.904232Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:53:45.466886Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:39:45.836079Z

Reference resolution

51 of 51 outbound references displayed

verified exact0
verified fuzzy0
unresolved51
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 68208e15-b1b8-43ee-b6f1-51357d229694 · outbound

This paper cites online" 'onlinestring :=.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model online" 'onlinestring :=

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:06.743591Z

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Observation 421e1458-fb18-4d8f-9a94-de25bac0f89f · outbound

This paper cites write newline.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model write newline

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:06.852222Z

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Observation 2ceec2c4-6c8e-4bd7-9574-2c20eab7f3f3 · outbound

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

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:06.919394Z

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Observation 894e88e7-e63a-4937-b84f-7455b006b3cc · outbound

This paper cites an unresolved cited work.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:06.994065Z

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Observation 2bfbd6ec-86c4-4971-939e-3e10d12b2ef0 · outbound

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

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:07.152919Z

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Observation 6a1d8979-ccc5-46a8-9fa4-e5d23aadb2ba · outbound

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

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:07.266788Z

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Observation b582f135-88a0-46b0-8295-7d6e1535d5f9 · outbound

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

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:07.340144Z

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Observation 65dfbec1-48ed-4253-ab04-18b7438fc778 · outbound

This paper cites Thinkless: LLM Learns When to Think.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Thinkless: LLM Learns When to Think

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:07.494215Z

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Observation 62378c28-3e4b-4467-afec-69742671cfb7 · outbound

This paper cites an unresolved cited work.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Unresolved cited work

Reference 9

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Observation 36d735ad-bb62-4f98-aa8a-decd1ec86a3c · outbound

This paper cites Efficiently Scaling LLM Reasoning with Certaindex.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Efficiently Scaling LLM Reasoning with Certaindex

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:07.680145Z

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Observation 2055cc70-f085-409e-aa56-6873f90f2ede · outbound

This paper cites OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems

Reference 11

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Observation 79fd9077-b484-4b1b-be1b-0d43ea2e9216 · outbound

This paper cites ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:07.852716Z

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Observation 0a505b18-50cf-4cfd-9d9f-189b25d2b832 · outbound

This paper cites an unresolved cited work.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:45:13.597244Z

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Observation 8c057826-bba7-4086-adb9-9b7aa3aba305 · outbound

This paper cites an unresolved cited work.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Unresolved cited work

Reference 14

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Observation accf01ce-e8e2-4f4e-9c75-2fc42dac7d20 · outbound

This paper cites Solving Quantitative Reasoning Problems with Language Models.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Solving Quantitative Reasoning Problems with Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:08.187391Z

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Observation 04bdb2ff-b466-4a84-9723-ce3e06415f39 · outbound

This paper cites an unresolved cited work.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Unresolved cited work

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:08.291449Z

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Observation 82e1ed63-295b-4791-a5f1-075fb30300a1 · outbound

This paper cites an unresolved cited work.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:45:13.446604Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8aa770e4-3ca1-4026-b226-c9449e09faf9 · outbound

This paper cites O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:08.644465Z

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Observation 69ba4bbf-ba5c-4e8f-8187-0bb675177a5a · outbound

This paper cites Reasoning Models Can Be Effective Without Thinking.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Reasoning Models Can Be Effective Without Thinking

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:08.793932Z

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Observation 485c7f5b-b443-4fd7-a2e7-27971216ecb2 · outbound

This paper cites CoT-Valve: Length-Compressible Chain-of-Thought Tuning.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model CoT-Valve: Length-Compressible Chain-of-Thought Tuning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:08.905293Z

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Observation d2619f22-8a04-4696-a026-252a28c127be · outbound

This paper cites s1: Simple test-time scaling.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model s1: Simple test-time scaling

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:08.994279Z

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Observation 1b8741ca-9107-4949-88fa-2f75d135840c · outbound

This paper cites s1: Simple test-time scaling.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model s1: Simple test-time scaling

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:09.112403Z

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Observation 7fe5da6d-ddba-4325-8c24-1e8d555b17f9 · outbound

This paper cites Self-Training Elicits Concise Reasoning in Large Language Models.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Self-Training Elicits Concise Reasoning in Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:09.240076Z

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Observation a31720ba-98b2-4ee1-9e9f-9f6d7e704508 · outbound

This paper cites an unresolved cited work.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:45:13.275219Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 40796972-6a4a-4a69-aa88-5373b11cf590 · outbound

This paper cites an unresolved cited work.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:45:13.139907Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a16f3062-2f13-4cae-ae75-08ac0a661d49 · outbound

This paper cites Demystifying Reasoning Dynamics with Mutual Information: Thinking Tokens are Information Peaks in LLM Reasoning.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Demystifying Reasoning Dynamics with Mutual Information: Thinking Tokens are Information Peaks in LLM Reasoning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:09.539739Z

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Observation 41da26e3-4e5d-4fb3-8c07-362fc3b80f4f · outbound

This paper cites Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:09.631754Z

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Observation 279663a4-a657-401e-9644-25a925020c9d · outbound

This paper cites an unresolved cited work.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:45:13.035297Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 52a889f5-2ceb-4629-8bc0-34390fc13591 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Proximal Policy Optimization Algorithms

Reference 30

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Observation 755fddc9-6bb6-4304-be57-6143548e60bf · outbound

This paper cites an unresolved cited work.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Unresolved cited work

Reference 31

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Observation 33f6c803-671d-4006-90bc-9d4e56765e4b · outbound

This paper cites an unresolved cited work.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Unresolved cited work

Reference 32

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Observation c194aa9c-c8f7-43fa-90f4-6bdb33150fc8 · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model HybridFlow: A Flexible and Efficient RLHF Framework

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:10.173720Z

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Observation d52c04d0-32e9-4730-b36a-609bd4a0a2d3 · outbound

This paper cites Thinking Fast and Right: Balancing Accuracy and Reasoning Length with Adaptive Rewards.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Thinking Fast and Right: Balancing Accuracy and Reasoning Length with Adaptive Rewards

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:10.270039Z

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Observation 5917b21d-e907-4223-9d63-eeaae4b7948c · outbound

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

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:10.340851Z

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Observation ec8767f2-68f1-4ff4-a6de-5811ea073ebe · outbound

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

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:10.436258Z

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Observation 79264837-4518-4277-86d1-df1aae92f151 · outbound

This paper cites an unresolved cited work.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Unresolved cited work

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:10.527430Z

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Observation 8d1f91d9-4df9-48bc-8cdc-80821d3e9df4 · outbound

This paper cites Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:10.621366Z

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Observation 3dfce824-a629-46ca-9da1-2e4a08c2e132 · outbound

This paper cites Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs

Reference 40

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Observation 08ce024a-be3d-4c97-b3cd-e3e98a347674 · outbound

This paper cites an unresolved cited work.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Unresolved cited work

Reference 41

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Observation 92a74131-c57e-44b9-a971-66f19d43b064 · outbound

This paper cites Learning to Reason under Off-Policy Guidance.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Learning to Reason under Off-Policy Guidance

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:10.938994Z

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Observation 7f3d562b-0297-4561-bb04-ccc41b59b4ba · outbound

This paper cites Qwen3 Technical Report.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Qwen3 Technical Report

Reference 43

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Observation 872e2f79-4196-49bd-a6c9-38c28b369cb5 · outbound

This paper cites an unresolved cited work.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Unresolved cited work

Reference 44

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Observation db3e6b06-037f-4f10-b78a-d18a448964aa · outbound

This paper cites Think When You Need: Self-Adaptive Chain-of-Thought Learning.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Think When You Need: Self-Adaptive Chain-of-Thought Learning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:11.207138Z

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Observation 05ed302e-0bbe-4534-99f0-626a5f61bc42 · outbound

This paper cites Understanding Aha Moments: from External Observations to Internal Mechanisms.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Understanding Aha Moments: from External Observations to Internal Mechanisms

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:11.280439Z

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Unavailable: canonical work link unavailable.

Observation 77eaa049-0bb4-4db0-b2fb-b5d018206285 · outbound

This paper cites Demystifying Long Chain-of-Thought Reasoning in LLMs.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Demystifying Long Chain-of-Thought Reasoning in LLMs

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:11.371418Z

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Observation e70b2234-1390-42a5-bbde-9a331c05bdb6 · outbound

This paper cites Distilling System 2 into System 1.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model Distilling System 2 into System 1

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:11.449407Z

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Unavailable: canonical work link unavailable.

Observation 4fbc789a-827c-4952-84db-831d9f418d9d · outbound

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Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:11.522799Z

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Observation 70018833-dd4f-4183-901f-6204e44b044d · outbound

This paper cites VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:11.615470Z

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Observation 56978212-369e-432f-9e87-27d9e39d2518 · outbound

This paper cites SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:11.711313Z

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Observation 8ae70e2a-a1d2-4824-8f88-39f0a119d810 · outbound

This paper cites AdaptThink: Reasoning Models Can Learn When to Think.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model AdaptThink: Reasoning Models Can Learn When to Think

Reference 52

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Observation 8fdd2d37-cf37-4fd9-b1ae-8ceaec1e1fce · outbound

This paper cites R1-Zero's "Aha Moment" in Visual Reasoning on a 2B Non-SFT Model.

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model R1-Zero's "Aha Moment" in Visual Reasoning on a 2B Non-SFT Model

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:11.904232Z

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Pith citing papers

Observation 21e56c44-1e3a-4ffd-ac55-aa98adf9a977 · inbound

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

Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model

Reference 156

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Source-reported events for the cited work

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Observation 7f79b699-5894-46b6-a425-456a09a5a222 · 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 Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model

Reference 38

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Observation a17936e1-9119-4918-ba97-0e95ef17d1d8 · inbound

TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning cites this paper.

TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-15T11:19:58.197620Z

Source-reported events for the cited work

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Observation 03e1983f-45d2-498f-a342-ce74e15f612e · inbound

DART: Draft-Agreement Routing for Training-Free Adaptive Thinking Budgets in Hybrid Reasoning Models cites this paper.

DART: Draft-Agreement Routing for Training-Free Adaptive Thinking Budgets in Hybrid Reasoning Models Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T10:39:45.837701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 67fb104e-45fe-405a-bb69-05330d975857 · inbound

Attention Degradation, Function Token Anchoring, and the Limits of Attention-Based Intervention in Large Language Models cites this paper.

Attention Degradation, Function Token Anchoring, and the Limits of Attention-Based Intervention in Large Language Models Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model

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