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Paper Citation Record · LEDGER

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models

As of 21 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 4 inbound Pith citation observations for arXiv:2505.13774.

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

pith.paper-citation-record.v1
2505.13774 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:14:41.426953Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:40:58.274985Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T11:16:03.303665Z

Reference resolution

36 of 36 outbound references displayed

  • verified exact0
  • verified fuzzy8
  • unresolved28
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 180ed1c2-402c-4c37-a06f-214f6bc498ca · outbound

This paper cites Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:14:41.290531Z digest=sha256:71d87142d70c2599b6f0bb9d33c4a39d3890a255eee24009a2542fe1ab4646d9

Observation 09e8b36f-5b37-48aa-b2dc-3dc0f300879c · outbound

This paper cites Claude 3.7 sonnet system card.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Claude 3.7 sonnet system card

Reference 2

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source=pdf_text observed=2026-08-15T20:14:41.295149Z digest=sha256:47c6d6da1629941c9e0cbe2ecfac3111c19d85c7e44be3eb443c3f9fba9a1bce

Observation 34c1dd22-d3b5-4e4d-bc39-676eeb569201 · outbound

This paper cites Chain-of-Thought Reasoning In The Wild Is Not Always Faithful.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Chain-of-Thought Reasoning In The Wild Is Not Always Faithful

Reference 3

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no resolver link, observed 2026-08-15T20:14:41.298997Z

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source=pdf_text observed=2026-08-15T20:14:41.298997Z digest=sha256:bef4b3ac8f4f68ac90e07b712642b620b88015b4c127358e13efff7250e2a51e

Observation 77de9039-6502-41bf-a255-06d87bedf54d · outbound

This paper cites Faithfulness Tests for Natural Language Explanations.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Faithfulness Tests for Natural Language Explanations

Reference 4

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no resolver link, observed 2026-08-15T20:14:41.303928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:14:41.303928Z digest=sha256:83c6f49748185ca290e15ab1e1957b44f1ee18a9e23fe6b0e4116ab944dad3ab

Observation 2d40209e-5118-41dd-a464-adbac0917f02 · outbound

This paper cites Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation

Reference 5

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no resolver link, observed 2026-08-15T20:14:41.308198Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:14:41.308198Z digest=sha256:8934fa3ffb47e56f8aa32fc9085cd710862d76988dd0e9062861d2df86be9554

Observation b22d84a6-ed08-4bf0-bcdf-323d9f46874d · outbound

This paper cites Reasoning models don’t always say what they think.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Reasoning models don’t always say what they think

Reference 6

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verified fuzzy
raw_fallback, observed 2026-08-15T20:14:41.908792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:14:41.312222Z digest=sha256:dff3f4c660b2871a903b8e493ed42ffa77060e3b6563c33efb1fc96db8b3b6d2

Observation d5c304b5-0ab3-438d-ad9b-0814d8cde3eb · outbound

This paper cites Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations

Reference 7

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no resolver link, observed 2026-08-15T20:14:41.316054Z

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source=pdf_text observed=2026-08-15T20:14:41.316054Z digest=sha256:f2576d839c8c37c6e7c62d4951c9ae52adea78a950b9c3e82f4bb134a40fd5d2

Observation be611117-1179-47ae-8c47-1f4361c5a698 · outbound

This paper cites Are DeepSeek R1 And Other Reasoning Models More Faithful?.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Are DeepSeek R1 And Other Reasoning Models More Faithful?

Reference 8

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no resolver link, observed 2026-08-15T20:14:41.319908Z

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source=pdf_text observed=2026-08-15T20:14:41.319908Z digest=sha256:220ccf29ce96664de569d0ce78005039cf808d11d79e83abce339785185b9c7b

Observation d7524a57-ff57-4bbc-8d8c-45020960caec · outbound

This paper cites Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs

Reference 9

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no resolver link, observed 2026-08-15T20:14:41.324673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:14:41.324673Z digest=sha256:943799dd073988da54a506e994cd4cb2af126acab0f7844c8e72d5810c7ba355

Observation ff84b833-00c3-4027-86ae-9077de8d2abc · outbound

This paper cites Are We Done with MMLU?.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Are We Done with MMLU?

Reference 10

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no resolver link, observed 2026-08-15T20:14:41.328082Z

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source=pdf_text observed=2026-08-15T20:14:41.328082Z digest=sha256:b62f0d47a39aa569b75f6af9d38f05ca3acd4ae6c23c27af8e0aae7232f0e897

Observation 676e1310-73f4-422b-810d-d2c0efafab99 · outbound

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

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 11

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source=pdf_text observed=2026-08-15T20:14:41.331856Z digest=sha256:e828cd87760d0bfdd3d672a000244034d34e7130784d14837dea5fd5c887525e

Observation 04280e3e-e42f-4dd6-96c5-464770b080b9 · outbound

This paper cites Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning?.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning?

Reference 12

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source=pdf_text observed=2026-08-15T20:14:41.335305Z digest=sha256:9d1709774c1d711719a1959a5f33aa614cbf3626faee9302939970efc4dbaf6f

Observation 4d8e926b-da02-4ab7-b2ac-6a6f3058a5bb · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Measuring Massive Multitask Language Understanding

Reference 13

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source=pdf_text observed=2026-08-15T20:14:41.339129Z digest=sha256:af2b3c0240dd963841d784053c91b26a9b419fc3468b5a74c88a379b57e4a0e0

Observation 954a10ba-9621-4d09-996a-cc4a790ad7ab · outbound

This paper cites an unresolved cited work.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Unresolved cited work

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:14:41.343027Z digest=sha256:e0db9130fcab5c058dcca31a16bbc131681dcbfa008612552acdac44952987a3

Observation ffa26ccd-20ab-45cc-b2ff-6a8cf947a190 · outbound

This paper cites OpenAI o1 System Card.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models OpenAI o1 System Card

Reference 15

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source=pdf_text observed=2026-08-15T20:14:41.346753Z digest=sha256:3339cd9d67b6aff669ec085e69f8cea0658842a291cbc95441ec4becbcddadf2

Observation fe3bdae5-dd59-4cf1-ab0d-2f5e22cdf334 · outbound

This paper cites Tulu 3: Pushing Frontiers in Open Language Model Post-Training.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 16

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source=pdf_text observed=2026-08-15T20:14:41.350010Z digest=sha256:d0d98a35259f7b8ef9cba5d2c39f9cebfc5eae2f765fac4e2246197caece2f4d

Observation 71115129-531f-45b2-aa7d-81532a1c224b · outbound

This paper cites Measuring Faithfulness in Chain-of-Thought Reasoning.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Measuring Faithfulness in Chain-of-Thought Reasoning

Reference 17

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no resolver link, observed 2026-08-15T20:14:41.353851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:14:41.353851Z digest=sha256:1d5e4aea36482b7109ab15ed279351a13a477a6d706227be560758d8416c896c

Observation bc740fec-8a73-49e0-88cc-b5d23f55ed70 · outbound

This paper cites Deepseek-r1 thoughtology: Let’s< think> about llm reasoning.arXiv preprint arXiv:2504.07128, 2025.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Deepseek-r1 thoughtology: Let’s< think> about llm reasoning.arXiv preprint arXiv:2504.07128, 2025

Reference 18

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source=pdf_text observed=2026-08-15T20:14:41.358379Z digest=sha256:168233aa922b6a6fdf01568adacd7530faea0e321802cd994cef4b0931ff4196

Observation f8be1fde-4913-4d0b-b731-03b5161b69d0 · outbound

This paper cites Openai o3-mini system card, 2025.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Openai o3-mini system card, 2025

Reference 19

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raw_fallback, observed 2026-08-15T20:14:41.881569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:14:41.362216Z digest=sha256:5aac7f3141f07f58172cf8ef1fa4209e856f32c23be9a97a29e37822eebde797

Observation 23665c23-49a9-46d2-a464-8e8be3187dcd · outbound

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

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Gpqa: A graduate-level google-proof q&a benchmark

Reference 20

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no resolver link, observed 2026-08-15T20:14:41.365513Z

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source=pdf_text observed=2026-08-15T20:14:41.365513Z digest=sha256:8e10c5d7f94f0ef0a1b7ed8c22b68c70ee0028bdb0b79e5f9d6c561eb4da0ae2

Observation 1837272f-d26e-473d-a771-f5df8ef09d57 · outbound

This paper cites On the hardness of faithful chain-of-thought reasoning in large language models, 2024.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models On the hardness of faithful chain-of-thought reasoning in large language models, 2024

Reference 21

Resolution
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raw_fallback, observed 2026-08-15T20:14:41.863620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:14:41.368929Z digest=sha256:79cfb3012c958e4b7455b060bd884ed99e86758151be39f503dc87b4d19b674f

Observation 6170b7d5-2f72-44ad-b10e-eb7df06f0933 · outbound

This paper cites Qwen3, April 2025.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Qwen3, April 2025

Reference 22

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source=pdf_text observed=2026-08-15T20:14:41.372113Z digest=sha256:30a3587229e1033fa6c1285dfbba079c8108c288b28117cee699a979f742aeac

Observation 559eca07-c3e8-4c0f-915f-34ebca8f758a · outbound

This paper cites Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting.Advances in Neural Information Processing Systems, 36:74952–74965, 2023.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting.Advances in Neural Information Processing Systems, 36:74952–74965, 2023

Reference 23

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source=pdf_text observed=2026-08-15T20:14:41.375670Z digest=sha256:b3fb21aaad2d8e0411b6a55a1ed4634f6c197c20851cbd94f764953e48bad1da

Observation 08edc20c-6720-4b79-8e12-a0e7bfd0a50c · outbound

This paper cites Thoughts are all over the place: On the underthinking of o1-like llms, 2025.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Thoughts are all over the place: On the underthinking of o1-like llms, 2025

Reference 24

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source=pdf_text observed=2026-08-15T20:14:41.379149Z digest=sha256:ddcfa3c2e2c556361f70f93694fe071b3c48a0c672da1bf7789f111edaebea56

Observation ae1cc333-b203-444a-85ab-89c5e24c75a0 · outbound

This paper cites Chi, Quoc V Le, and Denny Zhou.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Chi, Quoc V Le, and Denny Zhou

Reference 25

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source=pdf_text observed=2026-08-15T20:14:41.382460Z digest=sha256:658240926a48fbc2b7f78b1d9a7a252955380bda31af075cde6740c6d123499b

Observation b6f3aa6b-0698-4cef-a95f-f9b04bfcf147 · outbound

This paper cites Effectively Controlling Reasoning Models through Thinking Intervention.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Effectively Controlling Reasoning Models through Thinking Intervention

Reference 26

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source=pdf_text observed=2026-08-15T20:14:41.386011Z digest=sha256:31314913756280ba95b9b524029ca16f4d855a14b419ea3b1c84cf8dc2521a2f

Observation 421c5a21-ffa4-4c50-857d-3f51eeaaf7cd · outbound

This paper cites Dynamic early exit in reasoning models, 2025.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Dynamic early exit in reasoning models, 2025

Reference 27

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source=pdf_text observed=2026-08-15T20:14:41.390632Z digest=sha256:c2d5714d3fda7585dad0333696392de8b64368a532380784240b4f907cff501b

Observation b2c17e41-24d1-407d-922d-536833e06d41 · outbound

This paper cites Dissociation of Faithful and Unfaithful Reasoning in LLMs.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Dissociation of Faithful and Unfaithful Reasoning in LLMs

Reference 28

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no resolver link, observed 2026-08-15T20:14:41.393940Z

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source=pdf_text observed=2026-08-15T20:14:41.393940Z digest=sha256:970714d45f19bef9af2426f9dc5aee543940e97757ce7e105bd43dd8cd349e21

Observation 28e89627-5a55-4984-a927-aef5d81f7385 · outbound

This paper cites ‘json { “perturbed_option.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models ‘json { “perturbed_option

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:14:41.823096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:14:41.397893Z digest=sha256:c98dd3f8b7e795256054067c5b27a18ee235b086c5df4e8e8cc30ca96df4ad4f

Observation e6913a7e-b35e-469c-862f-1269869fcdc8 · outbound

This paper cites EXPLICITLY_CORRECTED.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models EXPLICITLY_CORRECTED

Reference 31

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raw_fallback, observed 2026-08-15T20:14:41.799510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:14:41.404703Z digest=sha256:3c72cdd76e8c58616aa1dd4b44b412759088b3ac1345b4a2c36a4e4c102260e9

Observation b6004830-518f-4290-999d-c6890e2b9e1a · outbound

This paper cites an unresolved cited work.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Unresolved cited work

Reference 32

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raw_fallback, observed 2026-08-15T20:14:41.786839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:14:41.408895Z digest=sha256:f9e81a3bd871f80378ef23157f72b12d299372b3eba57160f46ba6ed7ed66eb3

Observation c3a677ba-125c-4c57-9d50-d9484502c829 · outbound

This paper cites EXPLICITLY_CORRECTED.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models EXPLICITLY_CORRECTED

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:14:41.773528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:14:41.412598Z digest=sha256:33e4b7e0fcfc81a6a99fd1a7bed20bc603aa944918f03b5c2cc6a4d259abbda3

Observation a4f29cec-b128-4afd-8e38-b865fa1fc2ea · outbound

This paper cites an unresolved cited work.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Unresolved cited work

Reference 34

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unresolved
raw_fallback, observed 2026-08-15T20:14:41.760714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:14:41.415849Z digest=sha256:250ca889643f9b0b593a8ea55eb0938c8ecf53c162328dac0f81b5c85a05742c

Observation 58b87729-4033-4c3a-a41a-ec2482933956 · outbound

This paper cites EXPLICITLY_CORRECTED.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models EXPLICITLY_CORRECTED

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:14:41.747459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:14:41.419374Z digest=sha256:d90fff57cb589dc7d888745a93f48b3df9d18f57ac58606887efb159008f0752

Observation d0ca7ea5-c00f-46c3-a6a0-be65dcbd3da7 · outbound

This paper cites an unresolved cited work.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models Unresolved cited work

Reference 36

Resolution
unresolved
raw_fallback, observed 2026-08-15T20:14:41.811928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:14:41.423599Z digest=sha256:542f9cba12f80fa39194c3aac14022a9e857d5bf9895ae0f3b3c5905772415db

Observation 8b170b2c-2e20-41ba-9b3f-fbf88a4689c0 · outbound

This paper cites EXPLICITLY_CORRECTED.

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models EXPLICITLY_CORRECTED

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:14:41.735428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:14:41.426953Z digest=sha256:c739285a4e01efa5728f411f9238a78a906eb6bd56aceaea240dcbcc1f01bf32

Pith citing papers

Observation df660e94-2abd-4754-be99-949a2751cd90 · inbound

A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models cites this paper.

A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models

Reference 78

Resolution
unresolved
no resolver link, observed 2026-08-05T10:39:04.273817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:39:04.273817Z digest=sha256:9315b64eee7ca5293a6824a56b99d7e16d45279df1f5de818479a6a0f7607f09

Observation 7729dbc3-4d62-424b-b6a5-fa630867821b · inbound

FACT-E: Causality-Inspired Evaluation for Trustworthy Chain-of-Thought Reasoning cites this paper.

FACT-E: Causality-Inspired Evaluation for Trustworthy Chain-of-Thought Reasoning Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-11T11:16:03.312937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-10T15:04:23.688239Z digest=sha256:5f64e888bcf6d5329e9063e05c9e0ce1e6cb495f666d99f97b5e9823d9029a77

Observation 23ad4e73-b7ef-42c6-89ad-399ff2c6e743 · inbound

LLM Reasoning Is Latent, Not the Chain of Thought cites this paper.

LLM Reasoning Is Latent, Not the Chain of Thought Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-05-10T08:53:04.637091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-10T08:49:05.178087Z digest=sha256:3719c07ef107a61f93c00d80dd8d0e9d554f250f47cd3def44bbf3ba14d52a44

Observation a20b96d2-668e-4fd7-917e-c93fd6d2546b · inbound

Risky Business: Measuring The Faithfulness-Safety Tension cites this paper.

Risky Business: Measuring The Faithfulness-Safety Tension Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-05T13:40:58.274985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:40:58.274985Z digest=sha256:ed6f0fbc48fe0a58c84dbad55cdf1df9c0f6518bdec931344046ff30784f3f8e