Paper Citation Record · LEDGER

Self-Training Elicits Concise Reasoning in Large Language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 28 inbound Pith citation observations for arXiv:2502.20122.

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

pith.paper-citation-record.v1
2502.20122 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 28 of 28 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:18:00.832579Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T16:57:10.077886Z

Reference resolution

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verified exact0
verified fuzzy0
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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 25205ecf-15a6-42d9-8e4a-bb0f635f69bc · inbound

From System 1 to System 2: A Survey of Reasoning Large Language Models cites this paper.

From System 1 to System 2: A Survey of Reasoning Large Language Models Self-Training Elicits Concise Reasoning in Large Language Models

Reference 202

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T01:36:24.300467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9da65144-692f-460b-89dd-9707f04de681 · inbound

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

Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models Self-Training Elicits Concise Reasoning in Large Language Models

Reference 133

Resolution: verified exact; arxiv_id, observed 2026-05-14T01:29:56.627836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 288839eb-69ee-4030-a74f-913083a2f4e8 · inbound

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping cites this paper.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Self-Training Elicits Concise Reasoning in Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:00.832579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b353b1f4-4872-485d-aac9-0b57be157502 · inbound

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning cites this paper.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Self-Training Elicits Concise Reasoning in Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T15:06:58.800947Z

Source-reported events for the cited work

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Observation e065bada-4176-4ff3-a653-b36cdf8f6fa4 · inbound

Don't "Overthink" Passage Reranking: Is Reasoning Truly Necessary? cites this paper.

Don't "Overthink" Passage Reranking: Is Reasoning Truly Necessary? Self-Training Elicits Concise Reasoning in Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T14:54:52.618710Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1d34d6b-4868-4599-a546-7b4e13e749a5 · inbound

TrimR: Verifier-based Training-Free Thinking Compression for Efficient Test-Time Scaling cites this paper.

TrimR: Verifier-based Training-Free Thinking Compression for Efficient Test-Time Scaling Self-Training Elicits Concise Reasoning in Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T15:00:36.958178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 818e4f30-de9c-4d6c-938d-0dda9e8a237c · inbound

Not All Tokens Are What You Need In Thinking cites this paper.

Not All Tokens Are What You Need In Thinking Self-Training Elicits Concise Reasoning in Large Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:13.680515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 666450f5-a81d-4319-a12a-ca1c1b8e6800 · inbound

VeriThinker: Learning to Verify Makes Reasoning Model Efficient cites this paper.

VeriThinker: Learning to Verify Makes Reasoning Model Efficient Self-Training Elicits Concise Reasoning in Large Language Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T14:42:13.672381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e1c8307-a8e8-4f91-836a-98a20af4269f · inbound

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

Thinking Fast and Right: Balancing Accuracy and Reasoning Length with Adaptive Rewards Self-Training Elicits Concise Reasoning in Large Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:02.225034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efecde4f-3bcc-47c0-95b9-214a36b08dda · inbound

Efficient Long CoT Reasoning in Small Language Models cites this paper.

Efficient Long CoT Reasoning in Small Language Models Self-Training Elicits Concise Reasoning in Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:44.533614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9606b1ba-afb5-415c-818d-f2e611fb28dd · inbound

Can Past Experience Accelerate LLM Reasoning? cites this paper.

Can Past Experience Accelerate LLM Reasoning? Self-Training Elicits Concise Reasoning in Large Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:57.717067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f6c598e-85e7-4868-aca7-faf911a22672 · inbound

Self-Route: Automatic Mode Switching via Capability Estimation for Efficient Reasoning cites this paper.

Self-Route: Automatic Mode Switching via Capability Estimation for Efficient Reasoning Self-Training Elicits Concise Reasoning in Large Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T13:54:27.121587Z

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Observation d88c928a-1794-4b88-86e6-323e3c1d21b5 · inbound

PixelThink: Towards Efficient Chain-of-Pixel Reasoning cites this paper.

PixelThink: Towards Efficient Chain-of-Pixel Reasoning Self-Training Elicits Concise Reasoning in Large Language Models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:47.270161Z

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Unavailable: canonical work link unavailable.

Observation 5fb48e33-8d90-4743-a1d6-bc5beb2fc1aa · inbound

How Far Are We from Optimal Reasoning Efficiency? cites this paper.

How Far Are We from Optimal Reasoning Efficiency? Self-Training Elicits Concise Reasoning in Large Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:37.914205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb5706a8-691b-4e5d-8d06-aaba6dec87a4 · inbound

Wait, We Don't Need to "Wait"! Removing Thinking Tokens Improves Reasoning Efficiency cites this paper.

Wait, We Don't Need to "Wait"! Removing Thinking Tokens Improves Reasoning Efficiency Self-Training Elicits Concise Reasoning in Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:17.589835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29ebbd21-de7d-44f0-b298-a49443504355 · inbound

Fast on the Easy, Deep on the Hard: Efficient Reasoning via Powered Length Penalty cites this paper.

Fast on the Easy, Deep on the Hard: Efficient Reasoning via Powered Length Penalty Self-Training Elicits Concise Reasoning in Large Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T04:38:09.493118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97baf1fe-46c0-46c5-b0e7-d6940a123a4d · inbound

PREMISE: Scalable and Strategic Prompt Optimization for Efficient Mathematical Reasoning in Large Models cites this paper.

PREMISE: Scalable and Strategic Prompt Optimization for Efficient Mathematical Reasoning in Large Models Self-Training Elicits Concise Reasoning in Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T04:26:29.326145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77d123a3-49a9-450b-8f0e-accf6670caf2 · inbound

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement cites this paper.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Self-Training Elicits Concise Reasoning in Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:49.769760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model cites this paper.

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 069c2f97-ea07-498a-a2ab-585b262400f8 · inbound

CoRE: Enhancing Metacognition with Label-free Self-evaluation in LRMs cites this paper.

CoRE: Enhancing Metacognition with Label-free Self-evaluation in LRMs Self-Training Elicits Concise Reasoning in Large Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T19:16:31.342986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a50dcea-77aa-48c6-b002-3b3d8b4a68ee · inbound

A Practical Two-Stage Recipe for Mathematical LLMs: Maximizing Accuracy with SFT and Efficiency with Reinforcement Learning cites this paper.

A Practical Two-Stage Recipe for Mathematical LLMs: Maximizing Accuracy with SFT and Efficiency with Reinforcement Learning Self-Training Elicits Concise Reasoning in Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T18:26:08.506610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69a8c211-94ab-4ccf-b0f4-221adb9b1eb3 · 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 Self-Training Elicits Concise Reasoning in Large Language Models

Reference 138

Resolution: unresolved; no resolver link, observed 2026-08-06T17:54:17.514358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c6c0e35-743f-46f2-8ab0-d9da473d6e58 · inbound

Are Large Reasoning Models Interruptible? cites this paper.

Are Large Reasoning Models Interruptible? Self-Training Elicits Concise Reasoning in Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T10:07:52.495868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5729c83d-e51e-48b7-a44f-10b9e4e4d76b · inbound

When Less is Enough: Efficient Inference via Collaborative Reasoning cites this paper.

When Less is Enough: Efficient Inference via Collaborative Reasoning Self-Training Elicits Concise Reasoning in Large Language Models

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:41:42.320619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6b8974cf-f900-4ac9-b43c-c57a239fe505 · inbound

Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost cites this paper.

Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost Self-Training Elicits Concise Reasoning in Large Language Models

Reference 59

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T20:06:09.087964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4f58bf99-378a-4664-afc7-ba2855f6d45f · inbound

Thinking Economically: A Hierarchical Framework for Adaptive-Complexity Reasoning in LLMs cites this paper.

Thinking Economically: A Hierarchical Framework for Adaptive-Complexity Reasoning in LLMs Self-Training Elicits Concise Reasoning in Large Language Models

Reference 48

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T17:12:25.255665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 820a6b8f-b09c-47c1-b0e0-315a0ed0fecf · inbound

DyCon: Dynamic Reasoning Control via Evolving Difficulty Modeling cites this paper.

DyCon: Dynamic Reasoning Control via Evolving Difficulty Modeling Self-Training Elicits Concise Reasoning in Large Language Models

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T16:57:10.079263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9b6f061e-3d31-42d7-a182-b98ecf9fd697 · inbound

LISA: Linear-Indexed Sparse Attention for Efficient Long-Context Reasoning cites this paper.

LISA: Linear-Indexed Sparse Attention for Efficient Long-Context Reasoning Self-Training Elicits Concise Reasoning in Large Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-02T12:48:50.114743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.