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Paper Citation Record · LEDGER

Optimizing Length Compression in Large Reasoning Models

As of 20 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 11 inbound Pith citation observations for arXiv:2506.14755.

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

pith.paper-citation-record.v1
2506.14755 v2

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:56:12.637345Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:53:43.839273Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T23:26:22.299993Z

Reference resolution

42 of 42 outbound references displayed

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  • verified fuzzy0
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 81b239dc-171d-4559-a6c1-4ba00c24665c · outbound

This paper cites OpenAI o1 System Card.

Optimizing Length Compression in Large Reasoning Models OpenAI o1 System Card

Reference 1

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source=arxiv_source observed=2026-08-15T19:56:12.443346Z digest=sha256:06d712ee15ce5f5c9669ea890fdc1cf343e0c528e4c969ec03dd632110388506

Observation 56e554bc-9229-48c0-a92b-01660bcc93ed · outbound

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

Optimizing Length Compression in Large Reasoning Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2

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source=arxiv_source observed=2026-08-15T19:56:12.448323Z digest=sha256:4600c3e6320dd2013e39284758e313d88ddd1516a1270c3483e93233d1c12140

Observation 518c83dd-35b8-473d-bb53-d45f47d8c1b9 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Optimizing Length Compression in Large Reasoning Models Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 3

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source=arxiv_source observed=2026-08-15T19:56:12.452869Z digest=sha256:e5f0f6ba957f511e00f541e087f4abbbf05d0556dcd7eed724f76ea6080ca891

Observation 7abd0397-4f84-4a35-85dc-4d80cfc6594f · outbound

This paper cites Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models.

Optimizing Length Compression in Large Reasoning Models Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models

Reference 4

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source=arxiv_source observed=2026-08-15T19:56:12.456843Z digest=sha256:135ce8f1604ed406fa91fea2f340523b06576ec36f38c88ce0c8c4b7eee6d449

Observation 78b1a4f8-01ad-45a5-97c1-69d667a72861 · outbound

This paper cites CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution.

Optimizing Length Compression in Large Reasoning Models CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution

Reference 5

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source=arxiv_source observed=2026-08-15T19:56:12.461043Z digest=sha256:77c7f1eb912536367620a3d9eb385fbc0f9c69e4d4a25a3408d947ef24d103c5

Observation 17395788-7ef0-4e26-92e9-cbcb95dfe1f8 · outbound

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

Optimizing Length Compression in Large Reasoning Models Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models

Reference 6

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source=arxiv_source observed=2026-08-15T19:56:12.465322Z digest=sha256:fc7dac917695250a9b2c3e7aad31c15cdfe2ffc77860b3841c774e6d83e35c18

Observation a0ebe2e7-32cf-4e33-8afd-f4d20a68162e · outbound

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

Optimizing Length Compression in Large Reasoning Models L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning

Reference 7

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source=arxiv_source observed=2026-08-15T19:56:12.469941Z digest=sha256:53ec9f8bb919d6ac9de99cd059a2ef7fae76659e465cc0a10010d0f4fa4b0aaa

Observation 68fc4268-9176-4b2f-aec6-82449ea68dbb · outbound

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

Optimizing Length Compression in Large Reasoning Models Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 8

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source=arxiv_source observed=2026-08-15T19:56:12.474395Z digest=sha256:34e54361647dbba5893651dbdb7a4d3259fdb44ac975d46967de6dcd445aa660

Observation b8923d28-dd04-46db-ad36-11601ff6fe5e · outbound

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

Optimizing Length Compression in Large Reasoning Models Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

Reference 9

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source=arxiv_source observed=2026-08-15T19:56:12.480484Z digest=sha256:d0cbb05c945f2090e101965590cfff7d265a793516a7c048eccce9fc71a0b456

Observation 1742ebad-5d10-4d32-98a6-168b9f151259 · outbound

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

Optimizing Length Compression in Large Reasoning Models The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks

Reference 10

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source=arxiv_source observed=2026-08-15T19:56:12.485197Z digest=sha256:682df26aceb38bc5b6116bc7eac4a5bbc6b4993a9be85863349f5d2a2978a494

Observation 2e5396e3-7c3b-4415-9f98-5bb060ff807b · outbound

This paper cites Ada-R1: Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization.

Optimizing Length Compression in Large Reasoning Models Ada-R1: Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization

Reference 11

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source=arxiv_source observed=2026-08-15T19:56:12.490313Z digest=sha256:9729830db854a9810c096a461c340334550802ffe7327dcf7608ce108f1ee334

Observation e61dd383-e932-4a54-b175-a5fe66155073 · outbound

This paper cites Dast: Difficulty-adaptive slow-thinking for large reasoning models, 2025.

Optimizing Length Compression in Large Reasoning Models Dast: Difficulty-adaptive slow-thinking for large reasoning models, 2025

Reference 12

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source=arxiv_source observed=2026-08-15T19:56:12.495041Z digest=sha256:9e0ad1b879ed09d3962434a0cce4cb61631f5329ebea36894f66a04c92c38cf5

Observation ce912d4c-a7c8-4b24-9906-14c0b585f073 · outbound

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

Optimizing Length Compression in Large Reasoning Models ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning

Reference 13

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source=arxiv_source observed=2026-08-15T19:56:12.499151Z digest=sha256:39c7db74368dab368806457196e4fa9f44c9ecee88acb0f824996aae335e16d3

Observation a06fa339-3d84-441a-b1ae-653ce2e94270 · outbound

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

Optimizing Length Compression in Large Reasoning Models O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning

Reference 14

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source=arxiv_source observed=2026-08-15T19:56:12.504026Z digest=sha256:e3421dc9b6d4a4943b48c4f4a1bc96c3d4af7fb775c275f9bfca06da56f6c64e

Observation d674870c-9752-4d33-b49d-5c222c223aaf · outbound

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

Optimizing Length Compression in Large Reasoning Models Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 15

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source=arxiv_source observed=2026-08-15T19:56:12.508076Z digest=sha256:a6ae2c75ad2810be78ba7bc63ad200fa47e8ff926c660ff947f9c0fbf55895d2

Observation b3dd0930-fae6-4aff-ab72-27af1e240183 · outbound

This paper cites Qwen3, April 2025 a.

Optimizing Length Compression in Large Reasoning Models Qwen3, April 2025 a

Reference 16

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source=arxiv_source observed=2026-08-15T19:56:12.512274Z digest=sha256:d2e73830f0be4f8f3f477be1b7c01cca946c0c316d7f7cd5352d4706e1b65652

Observation 3c79b22f-b3eb-4abf-8776-ce6662dbe694 · outbound

This paper cites Qwq-32b: Embracing the power of reinforcement learning, March 2025 b.

Optimizing Length Compression in Large Reasoning Models Qwq-32b: Embracing the power of reinforcement learning, March 2025 b

Reference 17

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source=arxiv_source observed=2026-08-15T19:56:12.516538Z digest=sha256:42bd72c05736a3d08c80970e389bd0a181eb75c53ffe386ef60dfc5d5e81f85a

Observation 322138f9-5dc1-448b-9c92-588c67dad519 · outbound

This paper cites Yian Zhang, and Chris Alexiuk.

Optimizing Length Compression in Large Reasoning Models Yian Zhang, and Chris Alexiuk

Reference 18

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source=arxiv_source observed=2026-08-15T19:56:12.520598Z digest=sha256:47fdd9570fa267f3d851bd3bf1f728752e9533e2168950b708e88de4d9fc8dac

Observation 92d6ab52-214e-403f-ad6c-2d5ac74a2ba4 · outbound

This paper cites Understanding R1-Zero-Like Training: A Critical Perspective.

Optimizing Length Compression in Large Reasoning Models Understanding R1-Zero-Like Training: A Critical Perspective

Reference 19

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source=arxiv_source observed=2026-08-15T19:56:12.525489Z digest=sha256:534194bc0a2531dbf889575ec54098a7d7d75f05df244da5a31a4c8e88b8dbda

Observation 1ca758bb-4f08-4523-bbec-9c9bd35b547d · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

Optimizing Length Compression in Large Reasoning Models DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 20

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source=arxiv_source observed=2026-08-15T19:56:12.530153Z digest=sha256:2bbc9f9b0bd645fc3a9ff360ba064e645261a6b3fda3af351010c63f4024f284

Observation b2e71229-6465-42c0-abda-6c1b200e8ebe · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

Optimizing Length Compression in Large Reasoning Models Direct preference optimization: Your language model is secretly a reward model

Reference 21

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source=arxiv_source observed=2026-08-15T19:56:12.535861Z digest=sha256:e8f294895a8d97f2d2bd488b5eab3dfaa05d56d4a8513293c35149f92ee52248

Observation ed45114d-d98a-4985-97e2-23d3093f46aa · outbound

This paper cites an unresolved cited work.

Optimizing Length Compression in Large Reasoning Models Unresolved cited work

Reference 22

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source=arxiv_source observed=2026-08-15T19:56:12.540535Z digest=sha256:3a70a3d01998bfd87cc6e7bdbf03084e041f34d21cd13d89bb65b4b1b233c2b3

Observation c7fb6856-4cc7-4921-b209-dc1207c34b2e · outbound

This paper cites Gemini 2.5 pro.

Optimizing Length Compression in Large Reasoning Models Gemini 2.5 pro

Reference 23

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source=arxiv_source observed=2026-08-15T19:56:12.544588Z digest=sha256:38f38d635b78a62059d94dcdabe30d594a4e63a23fc3664c372a1d6650c38be2

Observation 7c5cf0c2-f5a6-402b-8eb4-9153c550960d · outbound

This paper cites Phi-4-reasoning Technical Report.

Optimizing Length Compression in Large Reasoning Models Phi-4-reasoning Technical Report

Reference 24

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source=arxiv_source observed=2026-08-15T19:56:12.548881Z digest=sha256:f0153791f7e37825a36f15df7a20e230d6358bf38116bc12a7c1e31edad54f12

Observation 0eaa993b-0236-4e17-804b-5bdc9794d37a · outbound

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

Optimizing Length Compression in Large Reasoning Models DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 25

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source=arxiv_source observed=2026-08-15T19:56:12.553304Z digest=sha256:5c9881bbd93296c1d5ecd6767949fb8898d81a4e93110f5b86d53a81299d0893

Observation b4864d86-7867-41ec-916c-5b71de176e37 · outbound

This paper cites Code-r1: Reproducing r1 for code with reliable rewards.

Optimizing Length Compression in Large Reasoning Models Code-r1: Reproducing r1 for code with reliable rewards

Reference 26

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source=arxiv_source observed=2026-08-15T19:56:12.557923Z digest=sha256:3c36bb0297b14290c42ea7e18a7b4932364f660cf3605467ea908ea80dfbb7ac

Observation 69bacfd6-94fb-4c93-9dc3-111adee4f039 · outbound

This paper cites REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization.

Optimizing Length Compression in Large Reasoning Models REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization

Reference 27

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source=arxiv_source observed=2026-08-15T19:56:12.562231Z digest=sha256:7765598d0c238329d922876a82e8517640478f49f781025bf1ca4a7ac9e7942e

Observation f4992d9e-8a12-47d9-b63c-d07aff99991c · outbound

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

Optimizing Length Compression in Large Reasoning Models CoT-Valve: Length-Compressible Chain-of-Thought Tuning

Reference 28

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source=arxiv_source observed=2026-08-15T19:56:12.566594Z digest=sha256:f87c5ae36a5e3dca1cf940401138c9ff1ef8f1af08a1f898dfed9a822819eb19

Observation 00d49e53-b5a8-47e3-992c-b6cf9da31e72 · outbound

This paper cites Training language models to reason efficiently, 2025.

Optimizing Length Compression in Large Reasoning Models Training language models to reason efficiently, 2025

Reference 29

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source=arxiv_source observed=2026-08-15T19:56:12.572177Z digest=sha256:a4fe95283c48cb16cc4caabdf0a4596d0e64db8c24896492eee1b42052b84bcf

Observation ebb13721-38da-489b-83e7-1e87cf4e0ecc · outbound

This paper cites Aytes, Jinheon Baek, and Sung Ju Hwang.

Optimizing Length Compression in Large Reasoning Models Aytes, Jinheon Baek, and Sung Ju Hwang

Reference 30

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source=arxiv_source observed=2026-08-15T19:56:12.577671Z digest=sha256:4010b2b6a0c3931b2f1efe7595ade27653ee2c8afeb988a33b0f02352b780af7

Observation f3ef6c34-5c71-45c4-b554-dbc7538ef52d · outbound

This paper cites Token-Budget-Aware LLM Reasoning.

Optimizing Length Compression in Large Reasoning Models Token-Budget-Aware LLM Reasoning

Reference 31

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source=arxiv_source observed=2026-08-15T19:56:12.584084Z digest=sha256:268c73652ac0e1c6edeff35e200c2e4551cd09835b56b479a89fde56a1acfe34

Observation a8941b35-eb1d-4f50-b36f-c5879ecd7c2c · outbound

This paper cites Reasoning Models Can Be Effective Without Thinking.

Optimizing Length Compression in Large Reasoning Models Reasoning Models Can Be Effective Without Thinking

Reference 32

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source=arxiv_source observed=2026-08-15T19:56:12.588793Z digest=sha256:d942870efe1961932e9845fa8e445216e5affee3827604b09cbc459efc567e77

Observation 1cd9036a-9c9f-4795-bffe-e36127970ddd · outbound

This paper cites Qwen2.5: A party of foundation models, September 2024.

Optimizing Length Compression in Large Reasoning Models Qwen2.5: A party of foundation models, September 2024

Reference 33

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source=arxiv_source observed=2026-08-15T19:56:12.594338Z digest=sha256:daa4e63e2f0eda085941c04e30eb1552c6ae178a0926d99b9e6b92ce2610f50c

Observation 607f402b-c8a2-44f6-a38a-e121ea528f64 · outbound

This paper cites Gemini 2.5 flash.

Optimizing Length Compression in Large Reasoning Models Gemini 2.5 flash

Reference 34

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source=arxiv_source observed=2026-08-15T19:56:12.599474Z digest=sha256:bd844ef5c09b2632be36c670c49a399f5836a5598eb3fb3942a1001d5ead4476

Observation 457a0db9-fef0-4b40-92f1-330fb6045945 · outbound

This paper cites Distilling System 2 into System 1.

Optimizing Length Compression in Large Reasoning Models Distilling System 2 into System 1

Reference 35

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source=arxiv_source observed=2026-08-15T19:56:12.606029Z digest=sha256:f3677629f1209e2f254034d5c95dd4ab3ab7166bfd548dce1eb37c5afdc2e6a9

Observation c5b974aa-2611-4b7b-b30b-9610dcab6756 · outbound

This paper cites The 23rd international conference on artificial intelligence in medicine (aime 2025).

Optimizing Length Compression in Large Reasoning Models The 23rd international conference on artificial intelligence in medicine (aime 2025)

Reference 36

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source=arxiv_source observed=2026-08-15T19:56:12.611256Z digest=sha256:f68cf5ac0cc4b6c92ba4b326d399f9fa588e8812032f1a57a5878db818186971

Observation 90c1919a-afef-46f5-bc11-c5847e72147e · outbound

This paper cites Let's verify step by step.

Optimizing Length Compression in Large Reasoning Models Let's verify step by step

Reference 37

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source=arxiv_source observed=2026-08-15T19:56:12.615273Z digest=sha256:1796e8e691bca6b55f37cf7fa3817669df7aa1060bfdf27089332b990ac3b166

Observation 74939c00-0f98-489b-9989-adfb81f786b7 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Optimizing Length Compression in Large Reasoning Models Training Verifiers to Solve Math Word Problems

Reference 38

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source=arxiv_source observed=2026-08-15T19:56:12.620496Z digest=sha256:158f0b8d1823d856dd5ab75ecccd834e7f7f62b0b85671fe82cfc73b51a6427a

Observation 75f6f2d6-203f-4572-8aa1-4ed91f9c2869 · outbound

This paper cites American mathematics competitions (amc).

Optimizing Length Compression in Large Reasoning Models American mathematics competitions (amc)

Reference 39

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no resolver link, observed 2026-08-15T19:56:12.625244Z

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source=arxiv_source observed=2026-08-15T19:56:12.625244Z digest=sha256:be1ecb685df478bfceb3d488bcf47819231c88f647b023f8ccf72704a85958f6

Observation 2dedafc0-be57-4275-9460-de5f9acf3226 · outbound

This paper cites Gpqa: A graduate-level google-proof q&a benchmark.

Optimizing Length Compression in Large Reasoning Models Gpqa: A graduate-level google-proof q&a benchmark

Reference 40

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no resolver link, observed 2026-08-15T19:56:12.628760Z

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source=arxiv_source observed=2026-08-15T19:56:12.628760Z digest=sha256:aff2c7d87c2e5fd885fb1ed1f8d6b25ed075f1582e48cd3d236b2a763fb0505a

Observation 4c1a46d1-2c0f-4cd3-b5a7-1c2be03c5ba7 · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

Optimizing Length Compression in Large Reasoning Models LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 41

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unresolved
no resolver link, observed 2026-08-15T19:56:12.632651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:56:12.632651Z digest=sha256:a5d4f2a438e9c51ebeb3eae77fa457d71713240111e6ef8c22f69d69281133c2

Observation 69cf9226-f2bb-40e1-bee6-fe3f9e5fa084 · outbound

This paper cites Trl: Transformer reinforcement learning.

Optimizing Length Compression in Large Reasoning Models Trl: Transformer reinforcement learning

Reference 42

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no resolver link, observed 2026-08-15T19:56:12.637345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:56:12.637345Z digest=sha256:299c0b9451faddc683067d37296e369e206b1b30130234081837ff2ecf98b786

Pith citing papers

Observation f8735614-7068-475b-93c4-0fa1d1519b56 · 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 Optimizing Length Compression in Large Reasoning Models

Reference 125

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metadata mismatch
arxiv_id, observed 2026-05-12T08:40:41.983748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-12T08:40:40.910461Z digest=sha256:77391ff221a3826d11817ac0060b355a175244aa3b0e88ea6d500d72313ceadf

Observation bc6ab941-4ace-481f-bf7d-10f787ff3438 · 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 Optimizing Length Compression in Large Reasoning Models

Reference 28

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no resolver link, observed 2026-08-06T17:53:43.839273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:43.839273Z digest=sha256:e637401137209f82d2c53b268243946788d0549b1e0bc1a6208e1399f6b8e167

Observation 6f69fd12-2d08-439a-a3b7-f2c67786832a · inbound

Reconsidering Overthinking: Penalizing Internal and External Redundancy in CoT Reasoning cites this paper.

Reconsidering Overthinking: Penalizing Internal and External Redundancy in CoT Reasoning Optimizing Length Compression in Large Reasoning Models

Reference 5

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no resolver link, observed 2026-08-06T05:14:37.093116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T05:14:37.093116Z digest=sha256:7b70ed04a2ebec2163aff811d62d6d96879d64a277d1ea5f79cc1f45a077d619

Observation e4466a45-860c-4843-ac05-09dab89649c4 · inbound

Failure Cases Are Better Learned But Boundary Says Sorry: Facilitating Smooth Perception Change for Accuracy-Robustness Trade-Off in Adversarial Training cites this paper.

Failure Cases Are Better Learned But Boundary Says Sorry: Facilitating Smooth Perception Change for Accuracy-Robustness Trade-Off in Adversarial Training Optimizing Length Compression in Large Reasoning Models

Reference 21

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no resolver link, observed 2026-08-06T05:08:11.949845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:08:11.949845Z digest=sha256:9ba33cb3a9f7b0c6a70a5f0e5e140c7163f8e1e5208710ebfb485009de4cebd0

Observation 30e538f9-3651-454a-afb4-d2086c23c59c · inbound

DiffAdapt: Difficulty-Adaptive Reasoning for Token-Efficient LLM Inference cites this paper.

DiffAdapt: Difficulty-Adaptive Reasoning for Token-Efficient LLM Inference Optimizing Length Compression in Large Reasoning Models

Reference 3

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verified exact
arxiv_id, observed 2026-05-18T04:32:23.125817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-18T04:31:40.360872Z digest=sha256:c683f86154034c301b99dc174b07a9fcb3b5e5770b80a5b4fd3136f26d9a51b5

Observation a6ceeae9-b58d-436c-8a4c-899f5ae407d6 · inbound

ConPress: Learning Efficient Reasoning from Multi-Question Contextual Pressure cites this paper.

ConPress: Learning Efficient Reasoning from Multi-Question Contextual Pressure Optimizing Length Compression in Large Reasoning Models

Reference 2023

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no resolver link, observed 2026-08-03T05:43:53.133475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:43:53.133475Z digest=sha256:94aac4a69ba0faec6439e21e6d9353a3d0195f535927523de8d8a269c6b4041a

Observation 45d69a29-7726-45c5-8cff-81d125af72f0 · inbound

Compress the Easy, Explore the Hard: Difficulty-Aware Entropy Regularization for Efficient LLM Reasoning cites this paper.

Compress the Easy, Explore the Hard: Difficulty-Aware Entropy Regularization for Efficient LLM Reasoning Optimizing Length Compression in Large Reasoning Models

Reference 8

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no resolver link, observed 2026-08-02T20:44:44.123438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T20:44:44.123438Z digest=sha256:d6c4ba2172043e9a61c2cf6dacd5f8e655ed5bbdc7bbff19260ae31cb5105660

Observation 0809db36-c9f9-448e-83e8-83e98ef3a1c4 · inbound

Implicit Compression Regularization: Concise Reasoning via Internal Shorter Distributions in RL Post-Training cites this paper.

Implicit Compression Regularization: Concise Reasoning via Internal Shorter Distributions in RL Post-Training Optimizing Length Compression in Large Reasoning Models

Reference 16

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metadata mismatch
arxiv_id, observed 2026-05-11T04:35:59.740619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-11T01:12:20.864362Z digest=sha256:75780f7eae28c78ef70a14b8030a38b57a8cded1a23967edd328bac6ed5f4414

Observation 916a2bcc-8e1c-4cc7-a6a6-229d9c34f5d9 · inbound

SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning cites this paper.

SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning Optimizing Length Compression in Large Reasoning Models

Reference 4

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metadata mismatch
arxiv_id, observed 2026-06-28T22:32:44.405373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-28T22:27:30.783923Z digest=sha256:e62b12bb4e611f7b2973e419138908df563f0dde371996e776d45f96e8ff6c24

Observation 1038563c-6048-40bb-a459-256a1afe1383 · inbound

Adaptive Latent Agentic Reasoning cites this paper.

Adaptive Latent Agentic Reasoning Optimizing Length Compression in Large Reasoning Models

Reference 32

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metadata mismatch
arxiv_id, observed 2026-07-01T23:26:22.302073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-06-28T14:24:22.855486Z digest=sha256:f43cc7859331c2ffb3b84cbe53531a7711fa7e7a2581b5f1233f619d29dcdb06

Observation 5b0eb710-8d34-4a72-aadd-14e25293efe4 · inbound

EvoThink: Evolving Thinking in Large Reasoning Models via Self-Pruning and Aha-Moment Preference Optimization cites this paper.

EvoThink: Evolving Thinking in Large Reasoning Models via Self-Pruning and Aha-Moment Preference Optimization Optimizing Length Compression in Large Reasoning Models

Reference 4

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no resolver link, observed 2026-08-01T11:17:40.210209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T11:17:40.210209Z digest=sha256:fdffc23167ddfd1dc8b37911e59649fa568e02dd3d2cae63360fe81b243c1edc