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Paper Citation Record · LEDGER

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models

As of 10 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2608.01319.

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

pith.paper-citation-record.v1
2608.01319 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:24:22.924827Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

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  • verified fuzzy4
  • unresolved24
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 347bfcac-3d03-4837-8cf8-b07267358fee · outbound

This paper cites Scales++: Compute Efficient Evaluation Subset Selection with Cognitive Scales Embeddings.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Scales++: Compute Efficient Evaluation Subset Selection with Cognitive Scales Embeddings

Reference 1

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source=pdf_text observed=2026-08-06T00:24:19.797452Z digest=sha256:d4f055d8d9712939c9425dbaf901cdd5d3e22d50308f631c1bbab5f8dee1b41b

Observation 71524d5d-909d-437b-b57d-6f360aa5cd2e · outbound

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

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 2

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source=pdf_text observed=2026-08-06T00:24:19.923359Z digest=sha256:f1847620a8e1c296918582cdfe9cef459abd2c1761ff46641cc5a5c9a1d53f42

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

This paper cites Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories.

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

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source=pdf_text observed=2026-08-06T00:24:20.051022Z digest=sha256:e8a8a5f30d4676d55cbb513b7d0ea0debe6bfaabeb5b1ec1f148917373f87d69

Observation 11abdd4f-421a-4ab3-9a6e-88fcfea68fa6 · outbound

This paper cites Deepseek-r1: Incentivizing reasoning capability in LLMs via reinforcement learning.Nature, 645:633–638, 2025.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Deepseek-r1: Incentivizing reasoning capability in LLMs via reinforcement learning.Nature, 645:633–638, 2025

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-06T00:24:25.324747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:24:20.154161Z digest=sha256:90b1cf9a4736fec014ee072f4f7557078a1f1bcb3c5b1c68baade5fffc662742

Observation 11b7f934-2f6a-48e3-bea1-59ff2ddd642b · outbound

This paper cites Meta Reasoning for Large Language Models.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Meta Reasoning for Large Language Models

Reference 5

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source=pdf_text observed=2026-08-06T00:24:20.280314Z digest=sha256:3d5f6fc1f609a970ec722a6fa46f883451e4673eac4258b04ae317942e808c37

Observation 48e81f47-23bf-4a75-86e9-b3bd2b6aed36 · outbound

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

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 6

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source=pdf_text observed=2026-08-06T00:24:20.388007Z digest=sha256:1d7393dd9b178f0aa464f6e0827e5cef2cd5eb4266d48cdf2cc6d7517ea9516e

Observation 5af84f3b-1f52-42ff-bdf1-7ff25022a0c1 · outbound

This paper cites Chain of mindset: Reasoning with adaptive cognitive modes.arXiv preprint arXiv:2602.10063, 2026.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Chain of mindset: Reasoning with adaptive cognitive modes.arXiv preprint arXiv:2602.10063, 2026

Reference 7

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source=pdf_text observed=2026-08-06T00:24:20.501488Z digest=sha256:0caca7eba3569a8894af652e7141efa088c525825c946ba2b291e9d7cd1f2da1

Observation 175e10cf-c894-45a2-9c97-cde927bdf183 · outbound

This paper cites Cognitive foundations for reasoning and their manifestation in llms.arXiv preprint arXiv:2511.16660, 2025.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Cognitive foundations for reasoning and their manifestation in llms.arXiv preprint arXiv:2511.16660, 2025

Reference 8

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source=pdf_text observed=2026-08-06T00:24:20.643266Z digest=sha256:351f77949163fe8d79dd5bc0bb401e269aa8d34a968b64354e8d1415f4608678

Observation 40602489-4aad-4ad5-a6c3-6679ee5096d7 · outbound

This paper cites Let’s verify step by step.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Let’s verify step by step

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-06T00:24:25.154731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:24:20.734841Z digest=sha256:dbd6c37a6cd16f31d497ba77f6421506e85487b4dd1e24a7406152c74fb6af07

Observation 1ec43431-1ac0-464d-b80c-8a1c10c66c86 · outbound

This paper cites MetaScale: Test-Time Scaling with Evolving Meta-Thoughts.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models MetaScale: Test-Time Scaling with Evolving Meta-Thoughts

Reference 10

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source=pdf_text observed=2026-08-06T00:24:20.814743Z digest=sha256:acc32f3ec98693ecabd912b13a0ec3f28fdd3ae3399aa01f6adc4760f0a98283

Observation 714785f7-bba5-4732-b973-6d7d78e4f6bb · outbound

This paper cites Self-Refine: Iterative Refinement with Self-Feedback.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Self-Refine: Iterative Refinement with Self-Feedback

Reference 11

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source=pdf_text observed=2026-08-06T00:24:20.959711Z digest=sha256:0770182bd789b9d9810a6f4b1867dccd8d5d9e546004708c916068054a17ae22

Observation ebb496f4-8533-4f66-a913-3a43f6690067 · outbound

This paper cites Learning to reason with LLMs.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Learning to reason with LLMs

Reference 12

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verified fuzzy
raw_fallback, observed 2026-08-06T00:24:24.815628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:24:21.028697Z digest=sha256:3e1e1ad2bd42d3b1859224a65e80f3dd83a139898c4064ae3a34c704c32dac47

Observation d57402b4-b4ef-46c8-9770-9d847ce8d8f5 · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 13

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source=pdf_text observed=2026-08-06T00:24:21.151325Z digest=sha256:dca7cf2ebb59d0e8883352919b693cadfc82331939471563e388ac010f395aa0

Observation b5bcb7d8-e603-44ca-994c-0dc535dff398 · outbound

This paper cites Reflexion: Language Agents with Verbal Reinforcement Learning.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Reflexion: Language Agents with Verbal Reinforcement Learning

Reference 14

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source=pdf_text observed=2026-08-06T00:24:21.205201Z digest=sha256:ad1957eb6c768d8f9ca3642bd0159cae67095835900f02204e3e45cb7254a2ae

Observation d5107c1a-ffe0-4d51-b383-561fc68276c3 · outbound

This paper cites Scaling llm test-time compute optimally can be more effective than scaling parameters for reasoning.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Scaling llm test-time compute optimally can be more effective than scaling parameters for reasoning

Reference 15

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source=pdf_text observed=2026-08-06T00:24:21.295458Z digest=sha256:c0da73715505928ae3ace674d3a284bc3896e2ecae4b92b3fcf211544562bafd

Observation acb953d9-807d-4d8e-a9b7-784b52a22660 · outbound

This paper cites Large language model reasoning failures.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Large language model reasoning failures

Reference 16

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source=pdf_text observed=2026-08-06T00:24:21.424188Z digest=sha256:bfebaa54f9cbb5093c6ed88186de0d61700424273c24904c8a7bc3dc29bdbdf7

Observation 6e1f51ea-0c81-4584-8333-3caf0ab3f80c · outbound

This paper cites Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMs.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMs

Reference 17

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source=pdf_text observed=2026-08-06T00:24:21.584761Z digest=sha256:fbfc1343bc2ec06e9da615c98e6c2ac8f13462aef3d763d94fd6fb22574e03b2

Observation 8cbaa9a8-18d6-443d-a9f1-8b2ad6d6da74 · outbound

This paper cites Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models

Reference 18

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source=pdf_text observed=2026-08-06T00:24:21.685650Z digest=sha256:1b6d1545787f686a42149aa5bf3fb4fdea52175b58bc330e91ab9c4b2e74b6dd

Observation 85a8301a-91f9-400f-8105-2a45452a911d · outbound

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

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 19

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source=pdf_text observed=2026-08-06T00:24:21.805694Z digest=sha256:0cffc6281cacf771de3aff744c9e3c4a6483216ae438bba9ef961f3af1526884

Observation b44fb4cf-a8df-48c4-9fd8-12c15f4ad5b3 · outbound

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

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 20

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source=pdf_text observed=2026-08-06T00:24:21.954848Z digest=sha256:05709a10ff946156fa73810ece4576d9de3fc74a592afe77dd4042f71dc18b6e

Observation 76bd5d9b-b59d-430d-b20e-935ac15d3d44 · outbound

This paper cites From efficiency to adaptivity: A deeper look at adaptive reasoning in large language models.arXiv preprint arXiv:2511.10788, 2025.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models From efficiency to adaptivity: A deeper look at adaptive reasoning in large language models.arXiv preprint arXiv:2511.10788, 2025

Reference 21

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source=pdf_text observed=2026-08-06T00:24:22.024276Z digest=sha256:356fffee584ae6d9370f9c32dd020ad5e2a796039c3c43e4ddacdfc723233ca9

Observation 86fb1fa7-e23a-48f6-90cf-c86c798b87d4 · outbound

This paper cites Position: Llms need a bayesian meta-reasoning framework for more robust and generalizable reasoning.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Position: Llms need a bayesian meta-reasoning framework for more robust and generalizable reasoning

Reference 22

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verified fuzzy
raw_fallback, observed 2026-08-06T00:24:24.644749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:24:22.109730Z digest=sha256:eb4790aaa10b7e9d99b3849fa5eee7340b982b6c04a2a065180a289d4ac5b40c

Observation 0cb1c78c-5d29-4377-8e2f-87a40216ffd3 · outbound

This paper cites Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models

Reference 23

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source=pdf_text observed=2026-08-06T00:24:22.264845Z digest=sha256:5479abd98f00d62d5f5d78fd4dc7d3a52acacb6c56f0957dd3abdb54ff164b0d

Observation 1a2e140f-236e-4ea4-af86-87fc2d5f2888 · outbound

This paper cites Tree of Thoughts: Deliberate Problem Solving with Large Language Models.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Reference 24

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source=pdf_text observed=2026-08-06T00:24:22.433798Z digest=sha256:c4336b0e792e0e4ff8b006e0b358800b980c2764939503ef08b05661e189c6c7

Observation 5bff5347-115f-47a1-be8d-4f4b0d0e1253 · outbound

This paper cites A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?

Reference 25

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source=pdf_text observed=2026-08-06T00:24:22.524838Z digest=sha256:38d0f7ccec2e5670e11c5f4b9d2b1f0b16c1483b3ca2def3b56e5e22d889a482

Observation 3755de05-78dc-41ff-b4b1-707c7f36e064 · outbound

This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 26

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source=pdf_text observed=2026-08-06T00:24:22.670185Z digest=sha256:abc4db5cdd7b323fc6d8fa30f1b3c1dc45223729b1a1814f8527cea18b06de30

Observation d1ab2660-eda6-4df4-9bd0-d0e279806dc3 · outbound

This paper cites General Scales Unlock AI Evaluation with Explanatory and Predictive Power.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models General Scales Unlock AI Evaluation with Explanatory and Predictive Power

Reference 27

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source=pdf_text observed=2026-08-06T00:24:22.764624Z digest=sha256:f83971a71fdf41f4b496c9be3c8e2245374faa49b54100d8bb9dc42920f4ad79

Observation 7921223b-3ee0-4633-9e0a-af2fe5e9b1f8 · outbound

This paper cites Scaling Test-time Compute for LLM Agents.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Scaling Test-time Compute for LLM Agents

Reference 28

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source=pdf_text observed=2026-08-06T00:24:22.837114Z digest=sha256:121fbc9840796d458bc47c5f050174046119bbecd46b4c01fb11a3319d13e1b1

Observation 63078ffd-64f3-4b2e-aa7b-9d8d12023694 · outbound

This paper cites Beyond Meta-Reasoning: Metacognitive Consolidation for Self-Improving LLM Reasoning.

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models Beyond Meta-Reasoning: Metacognitive Consolidation for Self-Improving LLM Reasoning

Reference 29

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local_arxiv, observed 2026-08-06T00:24:23.264841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:24:22.924827Z digest=sha256:d2accbc96389962d8a02140bb21b699896e531ef2a3004f35566b8046bc19043

Pith citing papers

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