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Paper Citation Record · LEDGER

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories

As of 14 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 2 inbound Pith citation observations for arXiv:2507.00711.

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

pith.paper-citation-record.v1
2507.00711 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:13:13.121454Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:24:20.051022Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T05:51:08.098335Z

Reference resolution

39 of 39 outbound references displayed

  • verified exact0
  • verified fuzzy2
  • unresolved37
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0ce28430-1447-4a20-b1cc-6df3890b4f6e · outbound

This paper cites an unresolved cited work.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Unresolved cited work

Reference 1

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:12:45.030972Z digest=sha256:5ab8127ae13d177d000d92e167d417103494bb06f62cc44663e24bef6da35b32

Observation 96204faa-602b-40d8-8148-6bd242a9b07a · outbound

This paper cites Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks

Reference 2

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source=arxiv_source observed=2026-08-06T21:13:12.722979Z digest=sha256:11c9531509600453d108ce2362a6f61e406e952fac14529b54cecf26b48b8138

Observation 54cf2e96-04ce-4fd7-abac-42db1f557aa2 · outbound

This paper cites Critique-out-Loud Reward Models.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Critique-out-Loud Reward Models

Reference 3

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source=arxiv_source observed=2026-08-06T21:13:12.739806Z digest=sha256:6e3e97513767cb521a8147ed5cfceee4ef30afa57a889c004c6c35e84a35dc59

Observation afe36068-81f1-4725-be62-bd3f6346308b · outbound

This paper cites Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models

Reference 4

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source=arxiv_source observed=2026-08-06T21:13:12.754676Z digest=sha256:030d9b7cf4b07991565a9cc83735d4fad7ec24ec9eac3e2202690e731ffda14d

Observation b140e735-dc0f-4d79-acd9-c8039dcdf2d8 · outbound

This paper cites CodePlan: Repository-level Coding using LLMs and Planning.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories CodePlan: Repository-level Coding using LLMs and Planning

Reference 5

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source=arxiv_source observed=2026-08-06T21:13:12.780233Z digest=sha256:375d5d372b2d4121562d8c117a5e1267191bb01a10eef96f805583c00a9a22d1

Observation 27a800bb-65c4-4fa6-ae2a-48e29170def8 · outbound

This paper cites an unresolved cited work.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Unresolved cited work

Reference 6

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-06T21:13:12.797219Z digest=sha256:319fe3f993a3d19313452f16c6558e54def99305bcd323f1646cd6ba3ab6fc9a

Observation 28e052de-9fed-4c6e-8f18-652c624f2982 · outbound

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

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 7

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source=arxiv_source observed=2026-08-06T21:13:12.810430Z digest=sha256:e347d111c64aae97ae922eba4e61bbf4736b512529196a21ac0df639cca1733c

Observation 33e92626-dde2-4ddb-87fd-6ed17f0d0c79 · outbound

This paper cites Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs

Reference 8

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source=arxiv_source observed=2026-08-06T21:13:12.820950Z digest=sha256:52e279a1b38bde6de76290fc9d149f4ce45503ea8c93430c725ea4536d96787b

Observation 40829b0d-c122-4f7c-90c6-6caa63f73831 · outbound

This paper cites Sparse Autoencoders Find Highly Interpretable Features in Language Models.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 9

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source=arxiv_source observed=2026-08-06T21:13:12.829504Z digest=sha256:d2944ec36c6f99f00f5cb0a891015205e18e078080ae09efb77cdbddc8f281e3

Observation e9a7d6db-5477-4974-9125-92b73594a13a · outbound

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

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 10

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source=arxiv_source observed=2026-08-06T21:13:12.842189Z digest=sha256:9056fc0c903afdbc4f55ec732d698e76947d5097c13d05f9096cba515429e41b

Observation 85e77101-b6ba-4207-bf9b-8c9ccf225721 · outbound

This paper cites Transcoders Find Interpretable LLM Feature Circuits.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Transcoders Find Interpretable LLM Feature Circuits

Reference 11

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source=arxiv_source observed=2026-08-06T21:13:12.849806Z digest=sha256:d1c4e7a9308a056b864938fb10480aa95c02524873e66d0c0f64c3d19750fc8d

Observation 286efa0d-5526-4a3c-879d-0ff2fbc83719 · outbound

This paper cites Interpretable Contrastive Monte Carlo Tree Search Reasoning.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Interpretable Contrastive Monte Carlo Tree Search Reasoning

Reference 12

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source=arxiv_source observed=2026-08-06T21:13:12.856172Z digest=sha256:f06b56561cc978c4b10275e71c8453aeb97310d8ca665c18188162d8b99edec1

Observation c480f1c5-9c68-4f25-9b8e-77c4d3e525d8 · outbound

This paper cites T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling

Reference 13

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source=arxiv_source observed=2026-08-06T21:13:12.864750Z digest=sha256:3566d37282833391eeadb1c046f872afea76f061a839c810529b738e6be2ca1b

Observation 7ceaeb7e-002e-4659-9f89-320e7d1bfcba · outbound

This paper cites LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks

Reference 14

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source=arxiv_source observed=2026-08-06T21:13:12.873393Z digest=sha256:b0ea5de2dea741e9786021761ffd262b4b95b61f297d5ecd9dbb4a155b037abd

Observation 20b71181-3b55-44b3-9e09-d7e19684da4b · outbound

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

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 15

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source=arxiv_source observed=2026-08-06T21:13:12.896156Z digest=sha256:d138974989d6f7c2001be5446852f7584747eb54e2806f2e7df1a2a6ab87f522

Observation 2ea5ab97-a966-4569-b43e-1040cb7710ba · outbound

This paper cites Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2

Reference 16

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source=arxiv_source observed=2026-08-06T21:13:12.903707Z digest=sha256:12e85280209e99bb08e39d6a5b3b7140f775f9bdbd10cd542f48e0e460c0566c

Observation da248f8f-3eb0-4973-a573-60992ab917bf · outbound

This paper cites an unresolved cited work.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Unresolved cited work

Reference 17

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no resolver link, observed 2026-08-06T21:13:12.912119Z

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source=arxiv_source observed=2026-08-06T21:13:12.912119Z digest=sha256:1e7a81dbad07af254eea384082ddac7bf9fd47294ea6033f876fa891e2856920

Observation 07a0e4b0-5561-4412-bd97-e5488e71df49 · outbound

This paper cites Tang, Manan Roongta, Colin Cai, Jeffrey Luo, Li Erran Li, Raluca Ada Popa, and Ion Stoica.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Tang, Manan Roongta, Colin Cai, Jeffrey Luo, Li Erran Li, Raluca Ada Popa, and Ion Stoica

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-06T21:13:12.920953Z digest=sha256:d5eea1e4052435e002124b5d3c92bcd1e4ba9331626b963ec158ae4b457ee4ba

Observation 8982f4d3-cdaf-442a-8f71-4c6e704d8f9a · outbound

This paper cites Reasoning Models Can Be Effective Without Thinking.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Reasoning Models Can Be Effective Without Thinking

Reference 20

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source=arxiv_source observed=2026-08-06T21:13:12.934750Z digest=sha256:b367c8ea52ee19093ddffd363ec7f038de2da1e0f2d032e1f44149f9d2f13bda

Observation 97966a76-53d8-4352-83d2-13ade9108578 · outbound

This paper cites s1: Simple test-time scaling.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories s1: Simple test-time scaling

Reference 21

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source=arxiv_source observed=2026-08-06T21:13:12.943940Z digest=sha256:6854eb5a0d41d0463141ffd6a486d614e34b2c7b500ac624b7d8e71c6e35e3c7

Observation c3ff820c-9dc1-4636-ba4b-cad3612ee30f · outbound

This paper cites an unresolved cited work.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Unresolved cited work

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-06T21:13:12.956511Z digest=sha256:0f0b4602bd90f9a967e11c6d5b0b7f031cb37b84b9b6c583c6d4ef56ac92299a

Observation c2a09a0e-f022-4ddd-82f0-b58aeceb7866 · outbound

This paper cites an unresolved cited work.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Unresolved cited work

Reference 23

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-06T21:13:12.962582Z digest=sha256:b085093059ca207965959ba88fe2b93f8762841a2da8c24d7f9cd81c330cb0b9

Observation f8a169c1-74fa-4ee8-b01a-005438ee54b1 · outbound

This paper cites an unresolved cited work.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Unresolved cited work

Reference 24

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-06T21:13:12.970392Z digest=sha256:367eb3b04e581a052ee300cd86c16789d6ce193652282b98a8b1e3e762e5b88d

Observation 4ca0d123-1808-4516-9361-94dde4e34aa5 · outbound

This paper cites Group Robust Preference Optimization in Reward-free RLHF.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Group Robust Preference Optimization in Reward-free RLHF

Reference 25

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source=arxiv_source observed=2026-08-06T21:13:12.979442Z digest=sha256:a6672395c603e0ea8b9fb48e5eb7ecc08e6f63dc331315e1a86b60cf1def4abd

Observation 9a3fc0a6-59c2-4f22-9ec6-c354e781314b · outbound

This paper cites Proximal Policy Optimization Algorithms.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Proximal Policy Optimization Algorithms

Reference 26

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source=arxiv_source observed=2026-08-06T21:13:12.991359Z digest=sha256:c4ef054bcf11d36c89f13f62f2617e3ad1a069722b4bda1ba443323612b28235

Observation a5a03000-ddb6-4db8-a58a-639361ea3c9c · outbound

This paper cites Optimizing Language Models for Inference Time Objectives using Reinforcement Learning.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Optimizing Language Models for Inference Time Objectives using Reinforcement Learning

Reference 27

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source=arxiv_source observed=2026-08-06T21:13:13.002002Z digest=sha256:ec24f50e0034cfefa6fda160322868d391442f7cca0eb854e4c04d24a9abbf67

Observation 775a8845-ebc0-4eff-818c-9e53b93e8c2e · outbound

This paper cites Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing

Reference 28

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no resolver link, observed 2026-08-06T21:13:13.009844Z

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source=arxiv_source observed=2026-08-06T21:13:13.009844Z digest=sha256:d2334a00a1b3ab0e9a9ea5a5072b2e1860bacf9ac3c4f567ae0c8988e6d14cb4

Observation 0eea2786-3587-46b6-8b5f-be4c58072569 · outbound

This paper cites LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 29

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source=arxiv_source observed=2026-08-06T21:13:13.017038Z digest=sha256:18317297add65fff4e04e1ef02f7d13f6f72fa1829c2c27ee44fbb8f54290d71

Observation 2e217cc9-f0fb-42d4-a558-6d7fcf82e397 · outbound

This paper cites Mixture-of-Agents Enhances Large Language Model Capabilities.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Mixture-of-Agents Enhances Large Language Model Capabilities

Reference 30

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no resolver link, observed 2026-08-06T21:13:13.023536Z

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source=arxiv_source observed=2026-08-06T21:13:13.023536Z digest=sha256:1b3b35a51756f9ed626f048f46225d93413779f8fa254a97631e708057ea24d0

Observation 5c404f8c-fe45-48a9-8648-53428f96f589 · outbound

This paper cites Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

Reference 31

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source=arxiv_source observed=2026-08-06T21:13:13.029610Z digest=sha256:d784eaa5bc9dfe63dffcd8db804c5a104c82822abb68085f7450a1aa71de6910

Observation e72b9d8a-2607-441c-8556-fd0ff6952531 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 32

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no resolver link, observed 2026-08-06T21:13:13.038644Z

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source=arxiv_source observed=2026-08-06T21:13:13.038644Z digest=sha256:9b3688ed2f2a6e24e2d43d81773df79c87719749018b6ebad9eb53c4f606f9e6

Observation 0fd5dae3-0522-43a6-ac4b-fe8238e94796 · outbound

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

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs

Reference 33

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source=arxiv_source observed=2026-08-06T21:13:13.046279Z digest=sha256:90a16eb34b9c07314556f4a49a066689e0825224c330d5e1dc7baf238ba84505

Observation f90ae4c5-9a03-4e7a-9329-81609b711af0 · outbound

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

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Chi, Quoc V Le, and Denny Zhou

Reference 34

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verified fuzzy
raw_fallback, observed 2026-08-06T21:13:13.645210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-06T21:13:13.051777Z digest=sha256:a1254d743fcabda1387f6d7352752c25c3ffd4844842ef5e093966c92936ff49

Observation bfeba1c9-4df4-465b-9e94-68c2ebfa3adc · outbound

This paper cites an unresolved cited work.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Unresolved cited work

Reference 35

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raw_fallback, observed 2026-08-06T21:13:13.561826Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-06T21:13:13.060486Z digest=sha256:a0b006dfec3c4df114fe327985af8eefc935782a8eeac313cf03ae9ad17710b5

Observation 6f9f34ad-ba7c-4b13-aa4e-b1ca80116534 · outbound

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

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Demystifying Long Chain-of-Thought Reasoning in LLMs

Reference 36

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source=arxiv_source observed=2026-08-06T21:13:13.066825Z digest=sha256:9423b6af3cb200318f820aeac84336b84c86ebedc7eacfa929d6e8c79404c72f

Observation ded393c9-1296-4f9d-90eb-923d45517bf6 · outbound

This paper cites Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Reference 37

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no resolver link, observed 2026-08-06T21:13:13.072280Z

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Unavailable: canonical work link unavailable.

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Observation b7acd3fa-06b0-4497-87a5-1012cee4c031 · outbound

This paper cites Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic Design.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic Design

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T21:13:13.077881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8fb864ed-aafa-4f21-baaf-9e5bd23f14f1 · outbound

This paper cites online" 'onlinestring :=.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories online" 'onlinestring :=

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T21:13:13.092764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:13:13.092764Z digest=sha256:6280a9e44a78a4bff78f4ff6621bb849a92c806984d4a9d6de9a937ae6afa3bd

Observation 84031adb-6967-4cab-9ef1-9efdd79a6d42 · outbound

This paper cites write newline.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories write newline

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-06T21:13:13.121454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:13:13.121454Z digest=sha256:386b0ffd9291a7dec5c9ebbad48b4b0c78f0139a712aba08794e7d2d67634cb9

Pith citing papers

Observation 346cb749-5c01-423c-9f43-4a55b660d881 · inbound

CLORE: Content-Level Optimization for Reasoning Efficiency cites this paper.

CLORE: Content-Level Optimization for Reasoning Efficiency Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-22T05:51:08.101200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation bb111e22-3a5f-45b7-8ec3-b659771fe20d · inbound

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models cites this paper.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T00:24:20.051022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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