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Paper Citation Record · LEDGER

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning

As of 7 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2507.10085.

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

pith.paper-citation-record.v1
2507.10085 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:52:38.697113Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+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

43 of 43 outbound references displayed

  • verified exact3
  • verified fuzzy3
  • unresolved37
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 732a3951-13b3-400a-a6ab-a0a655b187e0 · outbound

This paper cites The LLM Language Network: A Neuroscientific Approach for Identifying Causally Task-Relevant Units.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning The LLM Language Network: A Neuroscientific Approach for Identifying Causally Task-Relevant Units

Reference 1

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Observation 8d411b8c-9bbe-428a-ac2a-e322551cdbdc · outbound

This paper cites an unresolved cited work.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Unresolved cited work

Reference 2

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source=arxiv_source observed=2026-08-06T17:52:34.591359Z digest=sha256:0216176fc626801d61895889e8439d5d16ba24d868aa1ae17592f2a04c754581

Observation 1fff0f40-dcc6-4f01-b3c6-1423df7c193a · outbound

This paper cites Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future

Reference 3

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Observation d69f39e8-a48d-41c5-be31-aa06c4003da1 · outbound

This paper cites DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models

Reference 4

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Observation 8e77b96a-85ef-4723-b870-df232d7f7efb · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 5

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Observation b205983b-b8a1-4927-b9ea-02954720a57b · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Training Verifiers to Solve Math Word Problems

Reference 6

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Observation f33f08b3-2cb9-41e1-ab57-7d174a2ed617 · outbound

This paper cites Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 7

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Observation 2eb9339b-f04d-4a3a-b259-5d6c9aeaaf0b · outbound

This paper cites Improving complex reasoning with dynamic prompt corruption: A soft prompt optimization approach.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Improving complex reasoning with dynamic prompt corruption: A soft prompt optimization approach

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 64282cf8-2de8-43b6-9333-e8fbaed3ab9b · outbound

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Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Unresolved cited work

Reference 9

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Observation 31914b8a-da6a-4600-9aef-9901ba603330 · outbound

This paper cites an unresolved cited work.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Unresolved cited work

Reference 10

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Observation 20a7748e-04bf-435d-bf1c-b414a76852bf · outbound

This paper cites LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 11

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Observation 143d1385-c1c1-4389-b3b4-ff1ea8c7848a · outbound

This paper cites RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations

Reference 12

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Observation a812aa39-4782-4f0d-b5cf-a7fbfdec4854 · outbound

This paper cites MathPrompter: Mathematical Reasoning using Large Language Models.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning MathPrompter: Mathematical Reasoning using Large Language Models

Reference 13

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Observation 5e5d8b50-c768-4204-a96d-a81f63778d71 · outbound

This paper cites WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-Hop Inference.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-Hop Inference

Reference 14

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Observation c93bed37-f269-4c27-83d7-ab102c99bc35 · outbound

This paper cites an unresolved cited work.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Unresolved cited work

Reference 15

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Observation f6dbd82d-4d2d-413a-bb05-325ebdb58273 · outbound

This paper cites Focus on Your Question! Interpreting and Mitigating Toxic CoT Problems in Commonsense Reasoning.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Focus on Your Question! Interpreting and Mitigating Toxic CoT Problems in Commonsense Reasoning

Reference 16

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Observation b8273b8e-0196-475f-a1a4-2f8f3dd7357a · outbound

This paper cites Contrastive Decoding: Open-ended Text Generation as Optimization.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Contrastive Decoding: Open-ended Text Generation as Optimization

Reference 17

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Observation 2245e55b-5abc-4b03-b435-806a3e648e94 · outbound

This paper cites Let's Verify Step by Step.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Let's Verify Step by Step

Reference 18

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Observation 481f3ee3-0ecc-4adb-89a7-6d642d76278e · outbound

This paper cites Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems

Reference 19

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Observation 1ca9bdcc-28dc-4284-918d-cdca4ea567c6 · outbound

This paper cites Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning

Reference 20

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Observation d644df55-b0f1-4f8c-9590-7481ee85102a · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 21

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Observation d021b649-122f-44d0-a96b-927f68f16a67 · outbound

This paper cites Are NLP Models really able to Solve Simple Math Word Problems?.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Are NLP Models really able to Solve Simple Math Word Problems?

Reference 22

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Observation 91a8fad9-e4b1-4ffd-923c-15e8225ca15c · outbound

This paper cites Explain Yourself! Leveraging Language Models for Commonsense Reasoning.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Explain Yourself! Leveraging Language Models for Commonsense Reasoning

Reference 23

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Observation 5e318c42-5070-412c-80a9-02d3647da897 · outbound

This paper cites an unresolved cited work.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Unresolved cited work

Reference 24

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Observation 70516f0c-2af2-4dc1-b4e8-9b0ec4437d79 · outbound

This paper cites SocialIQA: Commonsense Reasoning about Social Interactions.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning SocialIQA: Commonsense Reasoning about Social Interactions

Reference 25

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Observation 85157fe8-e446-4571-9dd6-c786e27e0546 · outbound

This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 26

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Observation 2ac239cb-05e4-4171-9441-d822be91e4c9 · outbound

This paper cites CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

Reference 27

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Observation 8f329f3a-7acf-4048-8af8-cb2239bcf552 · outbound

This paper cites A Simple Method for Commonsense Reasoning.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning A Simple Method for Commonsense Reasoning

Reference 28

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Observation 397dfbe2-859e-451f-8c25-1d1107cd90a4 · outbound

This paper cites an unresolved cited work.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Unresolved cited work

Reference 29

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Observation e5c998d5-459a-4534-866d-002bb050f289 · outbound

This paper cites Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning

Reference 30

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Observation 40f84b27-7e4f-422b-a2f1-b6061f33771b · outbound

This paper cites an unresolved cited work.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Unresolved cited work

Reference 31

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Observation 1f34eb03-f76f-4537-ad9a-21f576d52a27 · outbound

This paper cites Advancing Parameter Efficiency in Fine-tuning via Representation Editing.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Advancing Parameter Efficiency in Fine-tuning via Representation Editing

Reference 32

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 82e2e057-d0e1-417f-8d07-f07a05d2bf59 · outbound

This paper cites ReFT: Representation Finetuning for Language Models.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning ReFT: Representation Finetuning for Language Models

Reference 33

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Observation ab851b5f-aac5-4e67-a9dd-894286ccd283 · outbound

This paper cites an unresolved cited work.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Unresolved cited work

Reference 34

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation aa322691-15b2-42e0-abfc-9af5e79d3880 · outbound

This paper cites Efficient Streaming Language Models with Attention Sinks.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Efficient Streaming Language Models with Attention Sinks

Reference 35

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Observation d02b12bb-2302-48d9-8193-4c7fc3235593 · outbound

This paper cites Don't take things out of context: Attention intervention for enhancing chain-of-thought reasoning in large language models.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Don't take things out of context: Attention intervention for enhancing chain-of-thought reasoning in large language models

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-06T17:52:39.757960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation e5dab6d5-20a2-484c-9c8f-a8d81d734d7c · outbound

This paper cites an unresolved cited work.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Unresolved cited work

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T17:52:38.172105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:52:38.172105Z digest=sha256:cd81d683ebcc0e3c133df409264b83f4878360e222ac420d8ee6f79a6e2c198a

Observation ad1d02ba-5b0a-4a91-a233-34fc83234a39 · outbound

This paper cites Unveiling and Harnessing Hidden Attention Sinks: Enhancing Large Language Models without Training through Attention Calibration.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Unveiling and Harnessing Hidden Attention Sinks: Enhancing Large Language Models without Training through Attention Calibration

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T17:52:38.257265Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 49408d19-6d42-43a5-a272-b7b5faa4c8a8 · outbound

This paper cites Instance-adaptive zero-shot chain-of-thought prompting.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Instance-adaptive zero-shot chain-of-thought prompting

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:52:39.619556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation d79ed181-2888-4232-876a-ebede6a7d7a9 · outbound

This paper cites Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-06T17:52:38.427574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 4836261b-bcd1-441a-b8f8-5439e68263a2 · outbound

This paper cites Representation Engineering: A Top-Down Approach to AI Transparency.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Representation Engineering: A Top-Down Approach to AI Transparency

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-06T17:52:38.498066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8f524e19-d8c0-4dee-b8e9-ea5253cd3082 · outbound

This paper cites online" 'onlinestring :=.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning online" 'onlinestring :=

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-06T17:52:38.596328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:52:38.596328Z digest=sha256:c1c8048fa63d89817c20a12759ca1e725439336706161778afbf1356671d15dc

Observation c10778ca-28c1-4922-93a6-65896815d539 · outbound

This paper cites write newline.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning write newline

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T17:52:38.697113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:52:38.697113Z digest=sha256:6605abd74ea0c8df007c61cbfc817c8bd8751f24f4e0f34df4183ddf0b5bc48c

Pith citing papers

No inbound Pith citation observations are available.