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Paper Citation Record · LEDGER

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning

As of 8 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2506.03673.

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

pith.paper-citation-record.v1
2506.03673 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:02:02.843031Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

30 of 30 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved30
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation be19672b-1f84-4828-befd-ad2b0d72ec11 · outbound

This paper cites GPT-4 Technical Report.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning GPT-4 Technical Report

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:00.137527Z digest=sha256:c67bfc89566ddfc788e9eac8f07ef45558666c0612851341e027a35a89735c71

Observation 70c51f84-f33e-44e3-8d1d-9447f97d16cb · outbound

This paper cites Give me a hint: Can LLMs take a hint to solve math problems?.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Give me a hint: Can LLMs take a hint to solve math problems?

Reference 2

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no resolver link, observed 2026-08-07T11:02:00.168903Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T11:02:00.168903Z digest=sha256:e8a565bc8ba1a69a1729707101ea051de4c552c9ec19fd6fc93b91b2acb21578

Observation 7e6908c5-01db-4798-b343-6566d5ebdd1b · outbound

This paper cites When can transformers reason with abstract symbols?.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning When can transformers reason with abstract symbols?

Reference 3

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:00.206114Z digest=sha256:bffb0df105cc94eb5dbaa939c54512ae4f689cf4550c37710865ae6905c57d5c

Observation 57a0303c-7fa5-4131-be70-30ccb253bc05 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Training Verifiers to Solve Math Word Problems

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:00.233239Z digest=sha256:1edc14e1a2c33d021ed177c65864e579856d1b4be2801d9816602510b4196721

Observation beed8e17-bc40-401c-b39f-d3d193e4eb34 · outbound

This paper cites The Llama 3 Herd of Models.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning The Llama 3 Herd of Models

Reference 5

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:00.252368Z digest=sha256:262920f10046943b7c2ba2cbfad66886c9656211fbb5be7909c84dcd9b6118f6

Observation bc929438-27f0-4add-ac79-1d00ca796142 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Measuring Mathematical Problem Solving With the MATH Dataset

Reference 6

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:00.290854Z digest=sha256:3d34209f65201127622ba79b61d87e5b22bacf12ae1c36899d8f3c76ffcd4e6d

Observation adb3f1cd-fbde-4fc3-865a-ffeb0431d18a · outbound

This paper cites an unresolved cited work.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Unresolved cited work

Reference 7

Resolution
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raw_fallback, observed 2026-08-07T11:02:03.576280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:02:00.331738Z digest=sha256:593b02b35c92a267ab5bcd4ded750ef70eeb62948d5371a5787a28e6c2e223b4

Observation 83598855-263a-472f-bd8f-5eae0f8ce271 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 8

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:00.400938Z digest=sha256:a6eb103d55badea779055a9d80c9b19b5f660caefb44931f71b57fa1ff69693c

Observation 5b62cbfd-1599-45e7-8811-498e62433c08 · outbound

This paper cites an unresolved cited work.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Unresolved cited work

Reference 9

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source=arxiv_source observed=2026-08-07T11:02:00.487840Z digest=sha256:368daa924f06d2fde3a1b0ea17cd38f2dd37c9a5654259e653b1628f4278e1ec

Observation 4c7ba98f-32e6-4a6f-8925-5c29c0048f34 · outbound

This paper cites LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning

Reference 10

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:00.557121Z digest=sha256:f272872a9a9903ec070e2362073ab6d79d808b493a22331cd9be317bc4c74bbd

Observation 83603fe5-38c3-4924-a5ad-dc52e9a5981c · outbound

This paper cites A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers

Reference 11

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source=arxiv_source observed=2026-08-07T11:02:00.688668Z digest=sha256:a6c16802cdc0e1e7f1051fd27f6984eac220275f987da03d7414a9c71304d0bd

Observation 02bf747d-1188-43d0-a9d7-6386ad102d83 · outbound

This paper cites GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

Reference 12

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no resolver link, observed 2026-08-07T11:02:00.827917Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:00.827917Z digest=sha256:b43c70c5031afb81e4e491583e55ecd653c3bc3ecd85d3208cffa3ff482ec1c6

Observation b5788d25-e546-451e-abbe-ef6c7ef5e8da · outbound

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

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Are NLP Models really able to Solve Simple Math Word Problems?

Reference 13

Resolution
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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:01.137552Z digest=sha256:4bdcffa783df5310f46af61fc5c8144949aac144227c3cbcbf9d61a9cebd68f2

Observation b9581a78-e97e-41fa-875b-c83b242a694f · outbound

This paper cites an unresolved cited work.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Unresolved cited work

Reference 14

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no resolver link, observed 2026-08-07T11:02:01.183293Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:01.183293Z digest=sha256:0ad7b82a7a452eb119f29cb6a322ee3b2ad749cd5e0d3fb28e6fd09314905f8f

Observation ba3bfe2f-d3cb-4537-8942-6532419f9723 · outbound

This paper cites Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models

Reference 15

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no resolver link, observed 2026-08-07T11:02:01.228446Z

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source=arxiv_source observed=2026-08-07T11:02:01.228446Z digest=sha256:e8aa0932dc0f38c73cf4afc3b54c1fa2a211292ada24dadcaf01a1e063664083

Observation 87d64018-0138-4f62-ae87-abc51341524a · outbound

This paper cites LLMs Can Plan Only If We Tell Them.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning LLMs Can Plan Only If We Tell Them

Reference 16

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:01.311063Z digest=sha256:4f08460c6875413f29f4cbbdc93e1acccc39ed49f88bc285ccd6882b0d8f976e

Observation 97838f15-b714-4468-a120-acebc51d688b · outbound

This paper cites an unresolved cited work.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Unresolved cited work

Reference 17

Resolution
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raw_fallback, observed 2026-08-07T11:02:03.401097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:02:01.499391Z digest=sha256:5f56cc2ae7cb1bb9810a978c97a89902dc023fc5196871804e6b17a8e3a652e1

Observation 9083ae48-e5d7-4800-9cf6-f4d20533b3a8 · outbound

This paper cites an unresolved cited work.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Unresolved cited work

Reference 18

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raw_fallback, observed 2026-08-07T11:02:03.264204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:02:01.837124Z digest=sha256:9b966138e9c705c4b2ac78e2e014f4b8a140c7919155cb506376ac871f340314

Observation ce1dee52-e749-4081-9035-e36a040c9c69 · outbound

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

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

Reference 19

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source=arxiv_source observed=2026-08-07T11:02:02.173182Z digest=sha256:9da04aff91e11fabf55ea07b7438df6d0f1dcce79007d82e3efec0345964b94e

Observation e8af0a02-17d0-4aff-b55c-94e39cf9baae · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Gemini: A Family of Highly Capable Multimodal Models

Reference 20

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source=arxiv_source observed=2026-08-07T11:02:02.217030Z digest=sha256:a05e43fbcf3c1f323097a82b34b47239486b381b0360b97b91206f6304f47e5b

Observation dfe9c7c5-2b83-4017-aeed-858fee6dc472 · outbound

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

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 21

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source=arxiv_source observed=2026-08-07T11:02:02.264233Z digest=sha256:6f170ebb11af98d7937a5bf0e907b9e0edbe9f8e7600593faa2000d02c7996b0

Observation ef28479d-5248-4c5c-a97a-bb2fdb0f1e1c · outbound

This paper cites an unresolved cited work.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Unresolved cited work

Reference 22

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:02.310359Z digest=sha256:ac7c421e430eff95c6f6030d93ffb6d6337ff644b07e1d7f93375049f00cecf1

Observation 8c010963-5a58-41d3-b993-684c0ff479a2 · outbound

This paper cites Faithful Logical Reasoning via Symbolic Chain-of-Thought.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Faithful Logical Reasoning via Symbolic Chain-of-Thought

Reference 23

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source=arxiv_source observed=2026-08-07T11:02:02.370781Z digest=sha256:0a03c7746d9649818e4237fa6ad458a0d6448776cabfaebf1e432a220dcef314

Observation 82f74f5d-8606-43a6-ac4f-7cda2145a8d0 · outbound

This paper cites Qwen2.5 Technical Report.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Qwen2.5 Technical Report

Reference 24

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no resolver link, observed 2026-08-07T11:02:02.436077Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:02.436077Z digest=sha256:937eb776487873113df1cbc1bc9d572df930d45b1c102a76458d1b8147f2a39d

Observation af548e36-0ac7-4764-acb6-60161a373c86 · outbound

This paper cites an unresolved cited work.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Unresolved cited work

Reference 25

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no resolver link, observed 2026-08-07T11:02:02.466492Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:02.466492Z digest=sha256:407d128cd540b794bfedca2459bd81f3849ead597ad3386d572238b0707d3a26

Observation 95784314-5ed3-4127-bd76-0e8a69f9faae · outbound

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

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning ReAct: Synergizing Reasoning and Acting in Language Models

Reference 26

Resolution
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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:02.534430Z digest=sha256:d06248dfb2b86dc818efeb5be3049b817d9cf3da1a1ab47f1fd1826586b42658

Observation 2ee71f24-2ff2-4bb7-bd19-aa4a8185bb92 · outbound

This paper cites Cumulative Reasoning with Large Language Models.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Cumulative Reasoning with Large Language Models

Reference 27

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:02.592115Z digest=sha256:95bb55d132002a67e8659f6957c98fa9f9c5973744680739fdb6ee840affee65

Observation df1a5c55-1d56-4322-ae30-cf80b355a23b · outbound

This paper cites Progressive-Hint Prompting Improves Reasoning in Large Language Models.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Progressive-Hint Prompting Improves Reasoning in Large Language Models

Reference 28

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no resolver link, observed 2026-08-07T11:02:02.694151Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:02.694151Z digest=sha256:4fe2d966d74aa93add4dd9c8fa273091c89ff913b1c776540c22a11d1f351602

Observation 6013e2ff-c538-49f9-b077-5d054c6f704e · outbound

This paper cites Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models.

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models

Reference 29

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:02.755601Z digest=sha256:3a11aa4d6a37a9c9d3e6c00481bc64ce1623353a7c4ff0769055e49bc7087c5b

Observation 5ab031d0-5439-449c-8215-75fafa9ec703 · outbound

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

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-07T11:02:02.843031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:02.843031Z digest=sha256:5c15e4086bc35d9c051ea8683380a314167ed7b3aa52f26aac5a0a5d33b4fb33

Pith citing papers

No inbound Pith citation observations are available.