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Paper Citation Record · LEDGER

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning

As of 18 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 2026-08-18T06:34:40.430872+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:fe100a75e9c6f977cce4169213831e883fc1584f8727bd5bdd39512bea502490

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

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:00.168903Z digest=sha256:b2a1c3e42d78d94154678699d41460dfbb19ffff50ef429e941a5f0b8b08808e

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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no resolver link, observed 2026-08-07T11:02:00.206114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:00.206114Z digest=sha256:efa2b5f4f8f8914cd770d0f79c9ce0ec485b364f8f17f604ded54d6a9a64f51e

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=sha256:98613e1666b4bb4e47fd0e72575b137c5dc7b0c07ea5050d96d006e6122be409

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=sha256:88253a9036515ae1eba027658d9852837de5ce76dd6532b92d08c71d4f126433

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:ddc91632bdfa40faab63ee7843aeec35cced9f9a2d98f4ebab0b7c4a86fccb9d

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
unresolved
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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T11:02:00.331738Z digest=sha256:cc0d2554df04451a4a82f8158940696c4b0d06f84a7404d3c8d33f06cac782aa

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=sha256:8119b376d239484ab690c41822b911eb688f1531b08c88b3a8a5d1a4b8a3356d

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-reported events for the cited work

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source=arxiv_source observed=2026-08-07T11:02:00.487840Z digest=sha256:f5dca4a6c37b040e0f35da6ce3fa44bb90ab96b060df1f693ae2cee8b797d499

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:00.557121Z digest=sha256:b91db72842135f5b4913f7961af4d91d2a961f9a8ca447195bdd6cf60a97714c

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

Resolution
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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:00.688668Z digest=sha256:4ff866569575d800f1a82fa14d970c7d597c9e1f201ee446c799b276f17fc811

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:00.827917Z digest=sha256:9a0a8284c264a7b80c97a521d778877c209f9cf0670c57d0625041efce3d59d7

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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no resolver link, observed 2026-08-07T11:02:01.137552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:01.137552Z digest=sha256:1c43b157c86b05750c83a9901308fc1292a85de35bd7969b526d61e3c24f2605

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:01.183293Z digest=sha256:a71821f3e74a4e86e710a601a03c8b10a2a50c6c4bf25fc99a9065bcee8962f4

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:01.228446Z digest=sha256:b3933fc8a1f6aa341e04a0fb41b404997fe43d85441579d99d93e4bfe1e0a721

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=sha256:3f2da8bc6615e0aa66e5a932be694ce3132596447aa99c554bc36d20f2a34f90

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
unresolved
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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T11:02:01.499391Z digest=sha256:60a8f9bb9169608145402ae997fbbeaf33010548848e37ffb97409bf147c6581

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

Resolution
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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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T11:02:01.837124Z digest=sha256:723fe3ffba2c641a6c379a53869aaac486b5b89cec15df22167ce074527e3a23

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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no resolver link, observed 2026-08-07T11:02:02.173182Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:02.173182Z digest=sha256:2fe582267170a8e41cafe4bfd71a3cf035d84b6a27a5ab351ac7b079f938f491

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:a23f4df01baebfbeec40c65ade5846a04e76991c6a4ad5d7210d99c91ec294b0

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:02.264233Z digest=sha256:3139888c9d86368851e1e1201e03afd2a10a84892fa69bbf7d9014fb438c7e64

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=sha256:67ffba7d1d2be64c6361368bc19510a2833a8d5d6e5906f49e089ef2d42aa539

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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no resolver link, observed 2026-08-07T11:02:02.370781Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:02.370781Z digest=sha256:4538727851d0b1990b0429a5524198c1fd4ffc72974b4568ded3cd6f9c9acdc9

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=sha256:1c90ff3c454c6b6036dbf441c0e3a143795df88f6a240d9358c83d3097f8676b

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:f6b428bbf5448b85819173e718f4e6359dde4802ac580598491d68fce67b5657

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:fe9a08fad60d6cea159984a43bbf8cb4c44d741d73bd930c1119aeaf38afc1b9

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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no resolver link, observed 2026-08-07T11:02:02.592115Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:02.592115Z digest=sha256:94a45cfb5f23bb37ff400af408f1d641a706abd20d88d89618faba480be61aa2

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

Resolution
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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:02.694151Z digest=sha256:eaa5210589cfbf3828658cb34cacaa4df77f54d59395eadb3f7a6d17761d3a5d

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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no resolver link, observed 2026-08-07T11:02:02.755601Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:02:02.755601Z digest=sha256:5d15be6a8301de77a21e735ed9146310e0d8e306a8b0b46791ea3c2db83d27ef

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=sha256:56e8a5120aa01edd1853f2211e015e36a5f5db23a4e741b2d7c005dce150585d

Pith citing papers

No inbound Pith citation observations are available.