Pith. sign in

Paper Citation Record · LEDGER

Optimizing Length Compression in Large Reasoning Models

As of 10 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 2026-08-07T00:16:41.969791Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

  • verified exact0
  • verified fuzzy4
  • unresolved38
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5f088494-e6d3-4e84-b081-e36e596379e3 · outbound

This paper cites OpenAI o1 System Card.

Optimizing Length Compression in Large Reasoning Models OpenAI o1 System Card

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:34.294760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:34.294760Z digest=sha256:5d37d4b247ff105352f24f5813e9dac69f7d64ea77b8d7107aba008429e29480

Observation b4439320-2078-4bf0-b3fe-86f786b38ebb · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:34.414892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:34.414892Z digest=sha256:59d6e06c6a9555aaacf3564a6e9b7134033c81f2c0e698061aa34229fdbfc249

Observation 89d51a15-4e5a-4647-9fec-6f37330f7719 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:34.559238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:34.559238Z digest=sha256:0a6290f66ac9d8dfa25f5bc6d914fbb98bec09df6054525af19d82ee632a6172

Observation 33c7ab18-a6ef-40bc-9d60-d1372ff176ce · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:34.774873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:34.774873Z digest=sha256:6320d0b4b40887a879aa737155476c30dfaecd17d237ac99922d428057d56990

Observation 3e96a6a6-9cb1-4f5f-99e5-ba5ddc646335 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:34.874959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:34.874959Z digest=sha256:697b52763c35c18a6079829bde84fc750d06c1048070fd525572f61771d743fc

Observation e9b21e14-5bb1-43b6-a3a1-7c78f6920f82 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:34.992049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:34.992049Z digest=sha256:a68cc548fcd98a5a9bcd38d749f7e2e97b7d6937debbaa7f19f81bdd060340a4

Observation f3584a7e-8a9a-4129-9cd8-a13062862e19 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:35.199105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:35.199105Z digest=sha256:83bb96caf76c06d2434c9d2d46588f8544c3f4b8d090dc1710bd2f1e0d7a4913

Observation 712f7048-528a-4983-8c02-fe24f208b870 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:35.304750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:35.304750Z digest=sha256:8be8c5414d61b215023b9d15af914d152cbc65964f00177130f82fcbf7dc92db

Observation 0dea06ee-b610-4019-8a8e-b261135bf6c2 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:35.420864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:35.420864Z digest=sha256:f3e31a2f44d095834d22240adfd067e4f0ded0065d904bf0b8cbb1a62bd99ffd

Observation 120bf619-4a70-4da5-8dd9-d6c89a99ea69 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:35.589321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:35.589321Z digest=sha256:35647e68dd4bf65ac830b9f71affb7ee7713f473884d40ce4e71a7d63dd73be7

Observation 1e88d015-97c9-42d1-abe1-132fa0ae08f6 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:35.851356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:35.851356Z digest=sha256:bdf2b8c7cb8c0ef839c84760da0ca6a94a6b85d848a76db3354ce8db4922ea1d

Observation 93ea9e9c-6744-4ae5-ba05-57ccc23e01d0 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:36.038609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:36.038609Z digest=sha256:6c367d6c7ea072e3dbbf3b00613855955013d24302f688135b442752e460cd73

Observation 75a755da-d28c-4fb1-b311-69119183b68c · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:36.272468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:36.272468Z digest=sha256:b4ae572058261c8901417e8de8612a8e4e1166769bc90a5e6a773e12b50688d7

Observation d4f074ad-f484-40d5-a52f-46a73029bace · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:36.460522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:36.460522Z digest=sha256:01adb433adc91efc166a2491a80e6885b2d74d1deeeaf30a5d76ff0d1e38d270

Observation 2961ff79-d1cf-4858-8fef-0ab6c80564bf · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:36.629497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:36.629497Z digest=sha256:fe766cda0e83a182349557f413298327a75c1b26e8e3c92c8254b6ac031345d2

Observation 7f89dd32-f6ee-47d2-9bd2-b45f823a1ba2 · outbound

This paper cites Qwen3, April 2025 a.

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

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:36.824756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:36.824756Z digest=sha256:5e515e11c902737d968ec0f908780b0f24830420cf22981cdc78bdf78109b9ae

Observation a4175192-5b96-4246-9807-955760c3ef7e · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:36.954415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:36.954415Z digest=sha256:067a94ac6e6a5c485a45db46234a63aaa38936685cfbcb9cf138cde5b2f0d505

Observation 9a06e2db-7bb2-4d27-af3d-2753ad4fb1a5 · outbound

This paper cites Yian Zhang, and Chris Alexiuk.

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

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:37.050135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:37.050135Z digest=sha256:3abcb3c137fd3c03580e097caa29d072369c50277970e87018e11d797435d34d

Observation d85e9749-6942-477e-9bf0-09814794ec59 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:37.170150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:37.170150Z digest=sha256:b9abb7452001e460cd79676568676e6cf92b1dc2f9627e5e4c7f8713d6f5adab

Observation 469a9ed3-d746-493e-b24d-b71a743a9376 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:37.300234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:37.300234Z digest=sha256:ff12931a8b52dc5fc2dd35562e4629c99d6042e44e009a9cf33ef05ae362410b

Observation 1084e068-3583-46d5-afa5-f139d2c780c7 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:37.586642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:37.586642Z digest=sha256:27a28ab9b62d8bef8670d37a71f1c07f639b3b5a3c3a16290a08d9e2ff40935a

Observation 8e43b95e-bfea-4e51-ba53-23576d3ad9df · outbound

This paper cites an unresolved cited work.

Optimizing Length Compression in Large Reasoning Models Unresolved cited work

Reference 22

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:16:46.942335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T00:16:37.774912Z digest=sha256:c487fb5c749d4487f8f095f0928fcc1d19e14d2e4e87b2954a8a694dbe8b0cca

Observation bf58d799-9ca6-40be-915e-140198c3ef84 · outbound

This paper cites Gemini 2.5 pro.

Optimizing Length Compression in Large Reasoning Models Gemini 2.5 pro

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:16:46.394029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T00:16:37.894763Z digest=sha256:d87a82b763263cf74d45a88ebab93d40495be33fc58aaa79e5beddeae9bba41f

Observation 78625bac-9355-4a45-bafa-f054c59bceb5 · outbound

This paper cites Phi-4-reasoning Technical Report.

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

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:37.970746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:37.970746Z digest=sha256:ab508c21b5b2b87fc87dbdbdb93a8cbd919259cdb8a293995acaf9796a7ebc93

Observation efb6ea44-37c4-45b7-8254-04f4c018447c · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:38.087650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:38.087650Z digest=sha256:c12bce07e83381e27b45706f567b630378c889f3208f319aa1160fc3c2f51c5a

Observation 3eb0a07e-f09d-43e0-bcc9-20b8c51841fc · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:38.240809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:38.240809Z digest=sha256:739fd689488af421ac478bb4f69204e25f555139ebd08f1e0fb169cb78c77f92

Observation 4db18859-aa05-471b-904c-f18d7c90c1e0 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:38.344742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:38.344742Z digest=sha256:f585cca539f49c74d993eb2f8c8a2b14d4a9a2fdf038a97442dbb3d830ae2c58

Observation 99b6b155-0ea3-45c2-ace2-ab26c7811333 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:38.481897Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:38.481897Z digest=sha256:24cf6fd69d4071d15fb54e0378438625ad578ec55d6380dfb53216536371a262

Observation e02e288c-9bd8-498c-b2ba-06e14266c327 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:38.644310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:38.644310Z digest=sha256:70af54f4be728023ff01350af697304e08768c746c6f739e89c26d7ed188454f

Observation d689a26d-22fa-4a44-90c2-c070b3a157bd · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:38.760328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:38.760328Z digest=sha256:bbaa46fd7bcd00658596d6ed6dc7473d8e7c296508acd126d94cd39df5e7440e

Observation e1fcd434-067d-4e68-99dc-ba21fdb8595a · outbound

This paper cites Token-Budget-Aware LLM Reasoning.

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

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:39.050549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:39.050549Z digest=sha256:e390289a24284748ff8d0b3b9272430777ec048d4dde1027c85c041b66152cef

Observation 4a6c70f4-7e4f-42f7-802d-5ed584658f60 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:39.489542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:39.489542Z digest=sha256:a635afde5beacc501e08fc086908a3dd230a7f6e0b68dc089bea96f4d3051b7a

Observation c14916c8-48cf-44cf-a3bb-c64dedfcb783 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:39.806016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:39.806016Z digest=sha256:ae2f5f5fef028b95bb029536afb70564f19a61250cace6b56f90e40606ae9af1

Observation ab962d8a-9425-4f46-872e-d90be2068435 · outbound

This paper cites Gemini 2.5 flash.

Optimizing Length Compression in Large Reasoning Models Gemini 2.5 flash

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:16:45.792290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T00:16:40.124618Z digest=sha256:9058992c053c6355019c7878f59fe2823d8d3c32c35be6d4d88b88c0b103a440

Observation 39688aed-efff-45b7-ad48-e0800b7fd641 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:40.762333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:40.762333Z digest=sha256:5a6e3be0837721ac413b6c17e4fbc375bcce59d54075d9f3433241723210c129

Observation 85106cc2-6c23-4b3d-95a9-82e2e17d417b · 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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:16:45.401357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T00:16:40.910314Z digest=sha256:2af52fb79bc5feba8c38e88f8c63936ed8df57fa82ab4457c741a9f8a8f599e3

Observation 3ff29671-5909-4025-b45b-11eadd088b61 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:41.034189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:41.034189Z digest=sha256:c085d5479559f349e646812c20fd56f11892aee7defd1a288e50248f74e7a5e7

Observation 97c36842-6304-4b99-b20a-50e3df120191 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:41.187901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:41.187901Z digest=sha256:e3ae4dfe42e632b1e92ca7dca3033d623491acbd4ac2b7f9459895a3b0b46298

Observation b0d67b5e-915b-43ae-be87-af0b61220d2c · outbound

This paper cites American mathematics competitions (amc).

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

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:16:45.052372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T00:16:41.417261Z digest=sha256:d10d651e799b206e81151ec3f4179e4e7a00f3bd38af5428c1a525a88ecd5c24

Observation da846939-5b46-4311-aad6-93bc54a38fb9 · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:41.599885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:41.599885Z digest=sha256:a64f08d591f80a3b6d0994b5cf6067fbeaf77c5e17102218566579a6034873fc

Observation 1486e9fe-5aa5-4901-ae83-19def43b822d · 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

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:41.810990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:41.810990Z digest=sha256:2f936c3fe5a98c0eb982c38a5ff9c4c2290b73daee96b64c961b9fe086167a36

Observation 07bddb4d-edf0-4b57-a3f4-3caad4c9c18c · outbound

This paper cites Trl: Transformer reinforcement learning.

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

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-07T00:16:41.969791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:16:41.969791Z digest=sha256:93419e52982cc32d423fb671aaf53572301b0f72b2dcd147528af4af38ed0dc9

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

Resolution
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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-12T08:40:40.910461Z digest=sha256:f1ed79bb0da7818499999e44a48109a2292c6adbd7d61921123e050000eb8cb3

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

Resolution
unresolved
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=sha256:4bba923d6b38d3e1232de17c60557181c26f0b5e2b9eba75dc1e93ae9b8fc4d2

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

Resolution
unresolved
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=sha256:4f0a41159f38ba5af8f79ee93d0506ebe4783625184d37380817a5967da91371

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

Resolution
unresolved
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=sha256:9147203c8a4eeca491926f2f17c2124f6f788e85f3ea2c7628032eac146f18ac

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

Resolution
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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-18T04:31:40.360872Z digest=sha256:843d6e6356c16eed08ada59f21182bef9992d238960e6e1ad91deac2d26247f3

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

Resolution
unresolved
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=sha256:9853f9019e66899a610b1be4d3670422618c05bcf8c07e4d10d0431c6ef22cea

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

Resolution
unresolved
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:cdcd0a5242d0b47553795e1586f60f2c2cfc900d3407d52a164f38e29953886c

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

Resolution
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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-11T01:12:20.864362Z digest=sha256:309976b8ff0f4add985e051c37bcb050b116236fcce0fcc90bb791ba0dea8cd9

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

Resolution
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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-28T22:27:30.783923Z digest=sha256:1654c771e48a83b8cf724d412280d199c2860d68d798c281651387d2e5c81315

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

Resolution
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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-28T14:24:22.855486Z digest=sha256:10d2e91d50d6ad60e3ea60f3f4c22525d775c03ab249a0afa61f3eaccbbd2ada

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

Resolution
unresolved
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=sha256:5d3cd97865c306a7896ebdf7e3f333773255b606e1f53f872ef35cda6411fd28