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Paper Citation Record · LEDGER

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models

As of 15 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 2 inbound Pith citation observations for arXiv:2506.04182.

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

pith.paper-citation-record.v1
2506.04182 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:52:45.240631Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:59:26.792078Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T17:54:18.778705Z

Reference resolution

35 of 35 outbound references displayed

  • verified exact0
  • verified fuzzy9
  • unresolved26
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3e02eb7e-a277-42cc-87da-44fdc81f5f81 · outbound

This paper cites Aime 2025 dataset.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Aime 2025 dataset

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:52:47.837113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T10:52:42.751765Z digest=sha256:0e8a0682d65895515e11d6856f1d336a34ed10b90d76dbdc9dcd1b8ef8e329bb

Observation e488b7b9-dc7f-4897-9369-59cfb4d22970 · outbound

This paper cites Amc 2023 dataset, 2023.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Amc 2023 dataset, 2023

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-07T10:52:47.640397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T10:52:42.819271Z digest=sha256:17d4fe878d4dae24f7fe35c0aa278ff495b594b7ef91261769af4d10e01b2481

Observation ea58a8b9-205e-4ddc-af18-6ec0deec0a09 · outbound

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

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Training language models to reason efficiently, 2025

Reference 3

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no resolver link, observed 2026-08-07T10:52:42.890260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:42.890260Z digest=sha256:b2d3faccd976f94fb1e7c1778c4d70085b714e0189460731486cbcbce4811b48

Observation b45e00b3-0d8b-415b-970b-6d95091dc51d · outbound

This paper cites Graph of thoughts: Solving elaborate problems with large language models.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Graph of thoughts: Solving elaborate problems with large language models

Reference 4

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no resolver link, observed 2026-08-07T10:52:42.958567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:42.958567Z digest=sha256:085ff1b0ade1e72fe34c516e8b4a3c9c11cb0e71ca214221843e5975610b62c3

Observation 12015bbf-0e73-4422-aea3-09288d883f9f · outbound

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

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:43.032330Z digest=sha256:b1cd86a3003128da2f4ef65c95f31353ebab9b6811e38439cc6002898e49556b

Observation b320e41c-677f-4b64-8571-9ad504432ac1 · outbound

This paper cites Compressed Chain of Thought: Efficient Reasoning Through Dense Representations.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Compressed Chain of Thought: Efficient Reasoning Through Dense Representations

Reference 6

Resolution
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no resolver link, observed 2026-08-07T10:52:43.087566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:43.087566Z digest=sha256:4bb19f3fd361acefcb67cca523b748c88ca2c2ab570a068df6263293fb291ae0

Observation 2f78083d-ee61-4867-b636-883911241b4d · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 7

Resolution
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no resolver link, observed 2026-08-07T10:52:43.135020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:43.135020Z digest=sha256:a74113e78144c46bb6563611dc5e5b79892337936716080df1d70fda02c95c90

Observation 9d94840a-fcf3-4179-9096-3bf28f37995e · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Training Verifiers to Solve Math Word Problems

Reference 8

Resolution
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no resolver link, observed 2026-08-07T10:52:43.197117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:43.197117Z digest=sha256:67491077ea2605132c566bd5e7f56e114705a994ade6c4a161a7a33ce6f4ec66

Observation 590ffeab-1d10-41f9-b184-c2adb6beb9c9 · outbound

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

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:43.262363Z digest=sha256:45b32d9ed23651cae4433b838cd64b6026d35b1e10f8cd99052b42b160a15367

Observation 3df8f4b6-63c7-4f29-b1ee-db42314184ab · outbound

This paper cites Token-Budget-Aware LLM Reasoning.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Token-Budget-Aware LLM Reasoning

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:43.358336Z digest=sha256:6df751c9fdc4d117ff93a7044c86ceb4ac9f904d720d5a6826c46ec07c9f542c

Observation e6dab186-6114-4532-b844-dd85a44fa5c9 · outbound

This paper cites Training Large Language Models to Reason in a Continuous Latent Space.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Training Large Language Models to Reason in a Continuous Latent Space

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:43.426385Z digest=sha256:225dea9b52c95ba8bf6a25f9323a8c07718846c882927e99fefb1058311ca979

Observation b104004b-8362-4a70-98e4-9968f0c770b9 · outbound

This paper cites Measuring massive multitask language understanding.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Measuring massive multitask language understanding

Reference 12

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no resolver link, observed 2026-08-07T10:52:43.499941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:43.499941Z digest=sha256:165af17baf37374e4c83aae8207f59e4622b5161f5424a86a89d9d377380650a

Observation 24d68348-f322-4e3b-af59-53b5ea542bf5 · outbound

This paper cites How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach

Reference 13

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no resolver link, observed 2026-08-07T10:52:43.571421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:43.571421Z digest=sha256:0ea27bc7420eddef28e7e74ff6282b3b3e1c4884984e133e4a3c32bade3a462c

Observation d96fd685-82f7-41db-9b93-0e66ce407fc1 · outbound

This paper cites Let's Verify Step by Step.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Let's Verify Step by Step

Reference 14

Resolution
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no resolver link, observed 2026-08-07T10:52:43.642199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:43.642199Z digest=sha256:e001c8bf64344047e29dc45e41ddb7a62559005ad21faea2dced2116f2b0f0f3

Observation 9acbe5aa-4f54-4fa6-a216-c3ac9d1bc68c · outbound

This paper cites Can Language Models Learn to Skip Steps?.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Can Language Models Learn to Skip Steps?

Reference 15

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no resolver link, observed 2026-08-07T10:52:43.731124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:43.731124Z digest=sha256:3d1dc7ff91cbdcd460dba6b8ce6956972cd8dadcd9b4ebaf98072781fe998620

Observation eb316e4b-aed5-4634-a520-c972fbdd79bb · outbound

This paper cites Thought Manipulation: External Thought Can Be Efficient for Large Reasoning Models.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Thought Manipulation: External Thought Can Be Efficient for Large Reasoning Models

Reference 16

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no resolver link, observed 2026-08-07T10:52:43.812067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:43.812067Z digest=sha256:fa43a03611b2a78afd77fba7f76a7fd31474c7cdf0d789839b9f2bf18c19b690

Observation f961489e-3869-4784-af32-f8d33b56c7e9 · outbound

This paper cites Reasoning models can be effective without thinking.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Reasoning models can be effective without thinking

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:52:47.429279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T10:52:43.863165Z digest=sha256:90b3a69ad735f8bf39390d242444d32111a84847c24602e684dbef984cb50814

Observation 7da4ff0c-8659-4c4e-980a-40cfeea82993 · outbound

This paper cites Reasoning Models Can Be Effective Without Thinking.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Reasoning Models Can Be Effective Without Thinking

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:43.931777Z digest=sha256:d15dc4c35ecee310f853ce08b2ae289576e7aa4a4e564e4274418d82cf12d8c2

Observation 5051bc73-093d-423f-bee0-ad47df6de5d2 · outbound

This paper cites s1: Simple test-time scaling.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models s1: Simple test-time scaling

Reference 19

Resolution
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no resolver link, observed 2026-08-07T10:52:44.016695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:44.016695Z digest=sha256:508f3cd3ce83aa9c1f8549188594680f87acdd0ccf7f4500900e0d732a687215

Observation c45e0a8a-2373-4eea-bacd-08e286482b93 · outbound

This paper cites Learning to reason with llms., 2024.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Learning to reason with llms., 2024

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:52:47.156985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T10:52:44.071438Z digest=sha256:10e5247d331476c3c4a05fa805bced0132dde6e0da9af0e98a1ea6167d2bcca8

Observation 0643ee1f-2868-48f9-9b64-e671a506c77a · outbound

This paper cites Opentriviaqa dataset, 2020.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Opentriviaqa dataset, 2020

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:52:46.911299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T10:52:44.129012Z digest=sha256:27f21125363c477b6db8a54abd7cc0322af365a362ea9d51c2af7a4aed56fcb9

Observation 2556b288-b3bb-4743-95fa-7971d878751b · outbound

This paper cites poetry dataset, 2024.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models poetry dataset, 2024

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:52:46.596847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T10:52:44.161132Z digest=sha256:36109b319514c3936181ac4d477a83219b700dafb29d257ef17a987d6f2b21e2

Observation 51ae1475-2fbf-4c95-9ffc-d479ee3d1d84 · outbound

This paper cites Qwen3: Think deeper, act faster, 2025.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Qwen3: Think deeper, act faster, 2025

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:52:46.263452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T10:52:44.224956Z digest=sha256:71a178a0535ccb42b739bb00cc9e9ac6a972935ad5a92212c089abd9f87ae9ce

Observation 931b8e21-369d-4411-a005-04199fbecf20 · outbound

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

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Gpqa: A graduate-level google-proof q&a benchmark

Reference 24

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no resolver link, observed 2026-08-07T10:52:44.295130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:44.295130Z digest=sha256:8c811827b0315f6fb0a02d0ec7183a3e60fc51e259c1c72801be869f99726719

Observation 88d772ed-4336-45de-83d2-0931fbdc0adc · outbound

This paper cites The benefits of a concise chain of thought on problem-solving in large language models.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models The benefits of a concise chain of thought on problem-solving in large language models

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:52:46.001285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T10:52:44.381173Z digest=sha256:f8bd849f3d7e92208651e281d521793a4f2eec7c1c6916124f1e3313fe2ce47a

Observation 49922946-a9e6-479f-9e12-5d4c2b699866 · outbound

This paper cites SocialIQA: Commonsense Reasoning about Social Interactions.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models SocialIQA: Commonsense Reasoning about Social Interactions

Reference 26

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no resolver link, observed 2026-08-07T10:52:44.456211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:44.456211Z digest=sha256:af6a21656b629d0c3e3a89d9ed29887efa1ba5f6e7d25f12f5fd42d08af1e8f9

Observation b348cb13-9d30-4f07-8795-e65cfa790a12 · outbound

This paper cites Manning, Andrew Ng, and Christopher Potts.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Manning, Andrew Ng, and Christopher Potts

Reference 27

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no resolver link, observed 2026-08-07T10:52:44.546182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:44.546182Z digest=sha256:86db3640363c7e442d946b48e3abcd7743c604a710405400a8dfd51eedadff1d

Observation 99377462-f1da-40c6-89b0-ba634be91df5 · outbound

This paper cites Brown, Adam Santoro, Aditya Gupta, Adri \`a Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Brown, Adam Santoro, Aditya Gupta, Adri \`a Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:52:45.793676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T10:52:44.672515Z digest=sha256:5ad1f8366950f2fd895613f4a05f43f6905121240c528c0125c247f7f85b3dfa

Observation dd3660a6-87d6-4a84-9189-cf6483b32d49 · outbound

This paper cites Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-07T10:52:44.759130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:44.759130Z digest=sha256:ee12c4434d1369915bab4e27f193eebbe615dda88cbf60ca27d58595083fd072

Observation 43725162-fbc6-4ba3-b3fa-e03f61513162 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Chain-of-thought prompting elicits reasoning in large language models

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-07T10:52:44.850130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:44.850130Z digest=sha256:8d4bbcbc83e60c586be4a88e85c6d4966924d4a8cb9d7a1f51b81d81640070fc

Observation 32e79d90-fd70-4a7e-aa2f-415848bd3664 · outbound

This paper cites When More is Less: Understanding Chain-of-Thought Length in LLMs.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models When More is Less: Understanding Chain-of-Thought Length in LLMs

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T10:52:44.856609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:44.856609Z digest=sha256:63fb7e3d960f08811fd38e1409e712d81d9c9ec3dd15d492acffa683f9bffb3e

Observation 4071d878-1ec9-438b-8666-f8d86b6df9e6 · outbound

This paper cites Tokenskip: Controllable chain-of-thought compression in llms.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Tokenskip: Controllable chain-of-thought compression in llms

Reference 32

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no resolver link, observed 2026-08-07T10:52:44.947071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:44.947071Z digest=sha256:1466ae012044d1eee8d22c076a3541ff5be29732326e07b0e9fce4063dd24b10

Observation 686d7cf5-c0e8-423d-8cd4-32940ec21008 · outbound

This paper cites Chain of Draft: Thinking Faster by Writing Less.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Chain of Draft: Thinking Faster by Writing Less

Reference 33

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no resolver link, observed 2026-08-07T10:52:45.010899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:45.010899Z digest=sha256:80a408b84ee0c3fbc7be40684d464262368d2367e888e1b26e021aa5c90dfbac

Observation 7ca7a04e-cd08-407b-96fa-eddcacfaaccf · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models Tree of thoughts: Deliberate problem solving with large language models

Reference 34

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unresolved
no resolver link, observed 2026-08-07T10:52:45.114159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:45.114159Z digest=sha256:63fea4b364d471ea1b74bcac21a4eb617bfa051d98bede2a3ba5497f36fba322

Observation 581f2270-aeb8-4e76-b25a-9e9b07e4afc5 · outbound

This paper cites write newline.

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models write newline

Reference 35

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unresolved
no resolver link, observed 2026-08-07T10:52:45.240631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:52:45.240631Z digest=sha256:c5c2ccafeb1d7ba268d78a2a50092d332236387aef94330a48b98a8b34d129af

Pith citing papers

Observation a098fc77-5446-40ee-8841-4e52fbd8b644 · inbound

Strategic Reflectivism In Intelligent Systems cites this paper.

Strategic Reflectivism In Intelligent Systems Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models

Reference 85

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no resolver link, observed 2026-08-07T12:59:26.792078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:59:26.792078Z digest=sha256:ad48db2332b20bb02b062a95d81a97cfcbc83224305724018e145dc731a7a22e

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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 Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models

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