Paper Citation Record · LEDGER

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers

As of 19 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2505.01482.

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

pith.paper-citation-record.v1
2505.01482 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:25:04.377515Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

36 of 36 outbound references displayed

verified exact4
verified fuzzy6
unresolved26
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation acec043e-cfc9-4b69-97fa-16e260c1f769 · outbound

This paper cites Large language models and cognitive science: A comprehensive review of similarities, differences, and challenges,.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Large language models and cognitive science: A comprehensive review of similarities, differences, and challenges,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.195103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 593aee1e-ebfd-46d4-87a7-8cdc1573700e · outbound

This paper cites Mulcogbench: A multi-modal cognitive benchmark dataset for evaluating chinese and english computational language models,.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Mulcogbench: A multi-modal cognitive benchmark dataset for evaluating chinese and english computational language models,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:25:05.115006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8bf398ee-b4ee-43c0-a675-4fd7dcb5c1e1 · outbound

This paper cites MulCogBench: A Multi-modal Cognitive Benchmark Dataset for Evaluating Chinese and English Computational Language Models.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers MulCogBench: A Multi-modal Cognitive Benchmark Dataset for Evaluating Chinese and English Computational Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.205720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6f9e33a-11c1-45e7-b5c0-4bfd56ea9a53 · outbound

This paper cites Large Language Models for Mathematical Reasoning: Progresses and Challenges.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Large Language Models for Mathematical Reasoning: Progresses and Challenges

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.210877Z

Source-reported events for the cited work

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Observation 45fd7c9d-9088-40cd-b8c6-2b113beaeab6 · outbound

This paper cites Explaining and Improving Contrastive Decoding by Extrapolating the Probabilities of a Huge and Hypothetical LM.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Explaining and Improving Contrastive Decoding by Extrapolating the Probabilities of a Huge and Hypothetical LM

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-16T04:25:04.878436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 67b79f32-aa41-491e-a11a-19021e393159 · outbound

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

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.223044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0878270b-e974-440d-8bab-237af887351e · outbound

This paper cites Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.233642Z

Source-reported events for the cited work

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Observation e4766948-bb99-4b54-b678-9b12ed289717 · outbound

This paper cites Can Stories Help LLMs Reason? Curating Information Space Through Narrative.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Can Stories Help LLMs Reason? Curating Information Space Through Narrative

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-16T04:25:04.805473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 06dd83c8-8cbe-4071-87f3-a8345c925577 · outbound

This paper cites MTMT: Consolidating Multiple Thinking Modes to Form a Thought Tree for Strengthening LLM.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers MTMT: Consolidating Multiple Thinking Modes to Form a Thought Tree for Strengthening LLM

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-16T04:25:04.782490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 43487ebd-d58b-459b-852a-f25c3d250cf9 · outbound

This paper cites Boosting Scientific Concepts Understanding: Can Analogy from Teacher Models Empower Student Models?.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Boosting Scientific Concepts Understanding: Can Analogy from Teacher Models Empower Student Models?

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-16T04:25:04.758837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ff256d9c-0c15-49a1-bfd6-d00575b6febe · outbound

This paper cites Refining llms outputs with iterative consensus ensemble (ice),.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Refining llms outputs with iterative consensus ensemble (ice),

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:25:05.097206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 55064bf4-dc7f-4e40-9d3a-4330e5cdb82b · outbound

This paper cites Reducing hallucination in structured outputs via retrieval-augmented generation,.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Reducing hallucination in structured outputs via retrieval-augmented generation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:25:05.081018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9620f0e1-189d-49e0-a970-9d16c2303b21 · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers ReAct: Synergizing Reasoning and Acting in Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.263446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2300ba1b-5d75-43ed-bcee-741246be9101 · outbound

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

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Chain-of-thought prompting elicits reasoning in large language models,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:25:05.064724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 290281cb-a849-4593-a0a6-f8841fd0a681 · outbound

This paper cites A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.283153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdee5789-b491-4957-96b4-68b87bc7d536 · outbound

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

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.278185Z

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Observation 33516f8a-b51a-4c9a-9291-795a4c6685e8 · outbound

This paper cites LogiCoT: Logical Chain-of-Thought Instruction-Tuning.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers LogiCoT: Logical Chain-of-Thought Instruction-Tuning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.292860Z

Source-reported events for the cited work

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Observation 7fc0a14e-dbae-4a8b-979f-f3de83041ca3 · outbound

This paper cites Automatic Chain of Thought Prompting in Large Language Models.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Automatic Chain of Thought Prompting in Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.287953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf4c5815-e81e-4998-8d7e-e00a51a0d56a · outbound

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

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.303066Z

Source-reported events for the cited work

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Observation ec92d587-6448-4f75-9e82-2878c912b550 · outbound

This paper cites Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.298071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77733ed7-f1bf-4fe6-beb2-578c56f8c341 · outbound

This paper cites Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.312904Z

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Observation c95b43dd-3b4c-4e8a-8378-033b931b4f96 · outbound

This paper cites Thread of Thought Unraveling Chaotic Contexts.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Thread of Thought Unraveling Chaotic Contexts

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.308006Z

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Observation d70a1d00-48fd-4fbb-aec9-41283acc265c · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.322507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01281dd1-20cc-4514-8887-c63953b9a7f1 · outbound

This paper cites Reasoning with Language Model Prompting: A Survey.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Reasoning with Language Model Prompting: A Survey

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.317669Z

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Observation 68012514-9383-4470-8ceb-6edf467a9a40 · outbound

This paper cites Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.331919Z

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Observation df949f86-6db7-413b-9b17-91aab154b535 · outbound

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

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.273030Z

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Observation b33ea9ae-5273-4810-ad34-0a58c468d5a5 · outbound

This paper cites MCC-KD: Multi-CoT Consistent Knowledge Distillation.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers MCC-KD: Multi-CoT Consistent Knowledge Distillation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.327172Z

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Observation 3e8a4c2e-2e26-4131-beda-98961296937e · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Evaluating Large Language Models Trained on Code

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.336847Z

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Observation 521159f5-98b7-4c59-a633-920af4d3e97a · outbound

This paper cites Blimp: The benchmark of linguistic minimal pairs for english,.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Blimp: The benchmark of linguistic minimal pairs for english,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:25:05.048572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 57d28444-cddd-414b-a441-a2dcefe5793e · outbound

This paper cites CogBench: a large language model walks into a psychology lab.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers CogBench: a large language model walks into a psychology lab

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.346594Z

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Observation a405042c-10b5-4bb6-a6f2-d243d2ed560e · outbound

This paper cites Measuring Progress on Scalable Oversight for Large Language Models.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Measuring Progress on Scalable Oversight for Large Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.351605Z

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Observation a37f3b7a-83e6-4a1d-be1d-49c62b963718 · outbound

This paper cites Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.356521Z

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Observation 68ab4da0-914d-4e9b-8af0-046c9aa7ecd4 · outbound

This paper cites MPNet: Masked and Permuted Pre-training for Language Understanding.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers MPNet: Masked and Permuted Pre-training for Language Understanding

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.361785Z

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Observation 45bccd2e-b927-4c33-8461-85847381a7b4 · outbound

This paper cites User’s guide to correlation coefficients,.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers User’s guide to correlation coefficients,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:25:05.030489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 37acd1c6-2814-4086-9c5d-b76b37d6ac38 · outbound

This paper cites s1: Simple test-time scaling.

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers s1: Simple test-time scaling

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.372104Z

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Observation 8409efcc-0113-4712-8966-1cffd7c555f5 · outbound

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

Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-16T04:25:04.377515Z

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Pith citing papers

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