Paper Citation Record · LEDGER

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance?

As of 22 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 3 inbound Pith citation observations for arXiv:2508.16695.

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

pith.paper-citation-record.v1
2508.16695 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:40:56.547891Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:15:17.849761Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-05T04:40:40.663804Z

Reference resolution

47 of 47 outbound references displayed

verified exact0
verified fuzzy24
unresolved21
parse uncertain1
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 87e6b6c7-e38f-4fa2-b7cf-efbaa83f6522 · outbound

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

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Chain-of-Thought Reasoning In The Wild Is Not Always Faithful

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T17:40:52.629513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49b64752-ab93-4f02-8b98-fd6ed42d6b4d · outbound

This paper cites Chain-of-thought is not explainability.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Chain-of-thought is not explainability

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:41:01.916536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2484697f-10d5-4220-94c5-b83338353d0c · outbound

This paper cites Interpretable Traces, Unexpected Outcomes: Investigating the Disconnect in Trace-Based Knowledge Distillation.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Interpretable Traces, Unexpected Outcomes: Investigating the Disconnect in Trace-Based Knowledge Distillation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T17:40:52.820356Z

Source-reported events for the cited work

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Observation c1d1f1c7-bb7b-4530-a370-b80c5dd911bf · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Qlora: Efficient finetuning of quantized llms

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T17:40:52.905695Z

Source-reported events for the cited work

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Observation fe9a403f-2d01-4fb6-a415-d0260a038288 · outbound

This paper cites Towards a rigorous science of interpretable machine learning, 2017.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Towards a rigorous science of interpretable machine learning, 2017

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:41:01.704737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bbcb9783-e2c6-4241-b756-2aba1cc30f0d · outbound

This paper cites A survey of methods for explaining black box models.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? A survey of methods for explaining black box models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T17:40:53.098238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 418880b8-7897-4d86-881b-075dcb38600a · outbound

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

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T17:40:53.154740Z

Source-reported events for the cited work

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Observation 223ab2ee-e450-44d1-a83a-5a7882716dc5 · outbound

This paper cites Nasa-task load index (nasa-tlx); 20 years later.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Nasa-task load index (nasa-tlx); 20 years later

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:41:01.476983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 73166613-e05c-47cb-aebc-c449b46a32d0 · outbound

This paper cites Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e58990ba-f15a-4289-8a8f-894ecc33d80e · outbound

This paper cites (how) do reasoning models reason? Annals of the New York Academy of Sciences, 2025.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? (how) do reasoning models reason? Annals of the New York Academy of Sciences, 2025

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:41:01.231369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0c2e99ed-6745-4f98-a530-ba1a49719cb1 · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Gonzalez, Hao Zhang, and Ion Stoica

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T17:40:53.521993Z

Source-reported events for the cited work

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Observation 297292cb-f749-4494-99de-d18ce2a7ab4e · outbound

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

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Measuring Faithfulness in Chain-of-Thought Reasoning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T17:40:53.597230Z

Source-reported events for the cited work

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Observation 78e1bcd0-5483-46e9-a582-74eca544022f · outbound

This paper cites Towards Better Chain-of-Thought: A Reflection on Effectiveness and Faithfulness.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Towards Better Chain-of-Thought: A Reflection on Effectiveness and Faithfulness

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T17:40:53.674949Z

Source-reported events for the cited work

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Observation be11dbdc-d2ce-4cc9-86f9-de97b68d924a · outbound

This paper cites Faithful chain-of-thought reasoning.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Faithful chain-of-thought reasoning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T17:40:53.768542Z

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Observation 0ef6de06-813d-43f0-88b6-3de4d4f7269e · outbound

This paper cites Teaching Small Language Models to Reason.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Teaching Small Language Models to Reason

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T17:40:53.941913Z

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Observation 5ade09d3-34dc-4273-b302-7a9c4626f04c · outbound

This paper cites Mann-whitney u test.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Mann-whitney u test

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:41:01.043476Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b0fd290f-bdb0-4515-b7a3-8fa19f40a33c · outbound

This paper cites Introducing gpt-oss.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Introducing gpt-oss

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:41:00.832857Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5854ae43-60be-495c-8d42-70b940f74dd0 · outbound

This paper cites Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T17:40:54.301852Z

Source-reported events for the cited work

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Observation 9211d40a-4c36-40de-968e-4acb9ce98c04 · outbound

This paper cites Distilling reasoning capabilities into smaller language models.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Distilling reasoning capabilities into smaller language models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:41:00.701279Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0e19193e-66a8-404b-a7d9-f90a756c574c · outbound

This paper cites Beyond Semantics: The Unreasonable Effectiveness of Reasonless Intermediate Tokens.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Beyond Semantics: The Unreasonable Effectiveness of Reasonless Intermediate Tokens

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T17:40:54.477554Z

Source-reported events for the cited work

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Observation 2c95500c-c04a-431f-9555-628e365a2009 · outbound

This paper cites Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning?.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning?

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T17:40:54.531346Z

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Observation 4a0070ed-d8e2-4d98-a3da-f25a6077f16a · outbound

This paper cites On the Hardness of Faithful Chain-of-Thought Reasoning in Large Language Models.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? On the Hardness of Faithful Chain-of-Thought Reasoning in Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T17:40:54.643811Z

Source-reported events for the cited work

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Observation 55476e6f-2dea-40f3-9858-679cb61bfa3e · outbound

This paper cites Beyond answers: Transferring reasoning capabilities to smaller llms using multi-teacher knowledge distillation.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Beyond answers: Transferring reasoning capabilities to smaller llms using multi-teacher knowledge distillation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:41:00.514760Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fc22e0d1-1904-4d7a-886a-8791a52b5728 · outbound

This paper cites Measuring chain of thought faithfulness by unlearning reasoning steps.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Measuring chain of thought faithfulness by unlearning reasoning steps

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T17:40:54.848543Z

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Observation 6d5dca39-ba9b-474e-9dd8-88b6755dab26 · outbound

This paper cites Transformers: State-of-the-art natural language processing.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Transformers: State-of-the-art natural language processing

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T17:40:54.949118Z

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Observation 8be85453-978e-4a2e-9aab-8f88075d2dfa · outbound

This paper cites How Interpretable are Reasoning Explanations from Prompting Large Language Models?.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? How Interpretable are Reasoning Explanations from Prompting Large Language Models?

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T17:40:55.021491Z

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Observation b73dc823-c4ca-4c86-898a-9cf8d025c83d · outbound

This paper cites an unresolved cited work.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:41:00.328245Z

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Observation e731bd27-3de8-4ef3-96af-10ae92396620 · outbound

This paper cites an unresolved cited work.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:41:00.165711Z

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Observation e253e1ae-0440-4266-a8f5-769a57cb0666 · outbound

This paper cites an unresolved cited work.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:40:59.979562Z

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Observation f2b5fe4d-b1a8-4488-a665-c0164e257079 · outbound

This paper cites After reviewing this information, participants rated statements about the reasoning on a 5-point Likert scale (Strongly Disagree–Strongly Agree).

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? After reviewing this information, participants rated statements about the reasoning on a 5-point Likert scale (Strongly Disagree–Strongly Agree)

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:59.848770Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 22e68441-345b-450d-bcbb-c5321f5bc51d · outbound

This paper cites Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:59.720558Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4cfe3f00-ae95-447d-bb8c-ecaa4fbb893f · outbound

This paper cites Limitations.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Limitations

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:59.568221Z

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Observation cb9d9840-6cea-45d5-9462-92486781e083 · outbound

This paper cites • All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? • All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:59.394660Z

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Observation 9d3658d4-028a-4dc6-91fe-ee2293570702 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Guidelines: • The answer NA means that the paper does not include experiments

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:59.173518Z

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Observation 4e84807c-a9fa-434e-8917-22d542da89e0 · outbound

This paper cites Guidelines: • The answer NA means that paper does not include experiments requiring code.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Guidelines: • The answer NA means that paper does not include experiments requiring code

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:58.995608Z

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Observation 066859cb-e78e-41fa-bfbe-1ea8bfedbba3 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Guidelines: • The answer NA means that the paper does not include experiments

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:58.832729Z

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Observation 6406ef20-d367-45b8-92d6-27af884663a1 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Guidelines: • The answer NA means that the paper does not include experiments

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:58.635174Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d807c86f-c902-4af9-b1b5-f471f6d3c789 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Guidelines: • The answer NA means that the paper does not include experiments

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:58.480810Z

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Observation f310aee9-d568-4369-b24c-986f7f05d0fd · outbound

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Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:58.307252Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation edc2669d-dca0-4689-a632-6d0c7202fe4c · outbound

This paper cites Guidelines: • The answer NA means that there is no societal impact of the work performed.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Guidelines: • The answer NA means that there is no societal impact of the work performed

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:58.106090Z

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Observation 16a31c5e-7bad-46c0-889d-20b966af6e1b · outbound

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Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:40:57.941765Z

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Observation e476810b-fd87-436b-90db-6fd0d7f4dca1 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not use existing assets.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Guidelines: • The answer NA means that the paper does not use existing assets

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:57.787453Z

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Observation 778ee659-733f-4b7b-a69b-30ab770592a7 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not release new assets.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Guidelines: • The answer NA means that the paper does not release new assets

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:57.642378Z

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Observation fadf3641-6e34-4f00-a060-066bb6df7440 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:57.478690Z

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Observation bf14b16a-60be-41c7-b973-fcbce712710d · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:57.300866Z

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Observation f9e51206-4f29-4450-828e-60ac8dde63e0 · outbound

This paper cites Answer: [Yes] Justification: All LLM-usage related details have been clearly provided in the paper where necessary.

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Answer: [Yes] Justification: All LLM-usage related details have been clearly provided in the paper where necessary

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:57.113058Z

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Observation 29a658ae-1523-47f3-a52a-0a0a4a1c20f2 · outbound

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Do Cognitively Interpretable Reasoning Traces Improve LLM Performance? Unresolved cited work

Reference 2025

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Pith citing papers

Observation cf0437e5-a985-43a8-a2d4-e4b215ac3c60 · inbound

Beyond Semantics: The Unreasonable Effectiveness of Reasonless Intermediate Tokens cites this paper.

Beyond Semantics: The Unreasonable Effectiveness of Reasonless Intermediate Tokens Do Cognitively Interpretable Reasoning Traces Improve LLM Performance?

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T20:15:17.849761Z

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Observation 0d77410e-5d69-4802-8989-f74ff8902c94 · inbound

R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling cites this paper.

R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling Do Cognitively Interpretable Reasoning Traces Improve LLM Performance?

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:46:05.638249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation af158f75-b00f-4414-bff8-ac7ea621536e · inbound

R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling cites this paper.

R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling Do Cognitively Interpretable Reasoning Traces Improve LLM Performance?

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-05T04:40:40.665781Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.