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Paper Citation Record · LEDGER

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models

As of 19 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 4 inbound Pith citation observations for arXiv:2504.13534.

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

pith.paper-citation-record.v1
2504.13534 v3

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:10:36.595794Z

measured 66 of 66 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:45:32.098095Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T18:40:02.347471Z

Reference resolution

62 of 62 outbound references displayed

  • verified exact1
  • verified fuzzy54
  • unresolved6
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f0642210-f71b-4517-9403-048804928df2 · outbound

This paper cites - Case: The example describes Tom, who can carry 6 plates at a time.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models - Case: The example describes Tom, who can carry 6 plates at a time

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.916068Z

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=pdf_text observed=2026-08-16T12:10:36.327838Z digest=sha256:187438dee08e487915ca43b3f6a6fa0a1ba794f9db4346e6d200233cb741c043

Observation 9331e4c2-2709-40cc-b1d9-bef3debe8aed · outbound

This paper cites - Case: The example describes Tom needing to pick up 15 plates from one table and 9 plates from another.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models - Case: The example describes Tom needing to pick up 15 plates from one table and 9 plates from another

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.897245Z

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=pdf_text observed=2026-08-16T12:10:36.333312Z digest=sha256:a24766a896cf8ba61542eb19ca9a38851cd95fd79093c153ae01ccd86887372e

Observation bf9bf691-cd1f-4446-a614-9e3b83b9919c · outbound

This paper cites InProceedings of the 2024 Con- ference on Empirical Methods in Natural Language Processing, pages 8916–8937, Miami, Florida, USA.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models InProceedings of the 2024 Con- ference on Empirical Methods in Natural Language Processing, pages 8916–8937, Miami, Florida, USA

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.977711Z

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=pdf_text observed=2026-08-16T12:10:36.266902Z digest=sha256:b7287ddd8e682b09086bd39f0c5a86f29970f16684cd354ae61358f9e08edc06

Observation 9affb8dd-a2c4-4ae2-a7c2-7ae36c6549c0 · outbound

This paper cites - Case: The example explains how Tom calculates his total number of plates.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models - Case: The example explains how Tom calculates his total number of plates

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.854944Z

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=pdf_text observed=2026-08-16T12:10:36.343732Z digest=sha256:ef9200f3e620f97c8a8c40c9f37aa02c8b00df36cce1d6b54419bbbc0f205d42

Observation f92552d2-0390-4684-903d-d01e1480eff4 · outbound

This paper cites Assuming the speed of the first person is v kilometers per hour, what is the speed of the second person in kilometers per hour?.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Assuming the speed of the first person is v kilometers per hour, what is the speed of the second person in kilometers per hour?

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.836431Z

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=pdf_text observed=2026-08-16T12:10:36.348757Z digest=sha256:34af349bfe25eba8054e59fb3a0f2b046b2538ec560a0426ff14af9d3677c750

Observation 67699260-f4f5-4d32-b0e1-ac29675b25cd · outbound

This paper cites Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-16T12:10:36.293624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:10:36.293624Z digest=sha256:9c6540ea243bb341a26a71b9131edd69a3dbf4d0fa2d2c06b8dc445bfd225021

Observation e0366b82-fa5c-4dc9-bdf4-07cefc24645b · outbound

This paper cites Retrieval-Augmented Generation for AI-Generated Content: A Survey.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Retrieval-Augmented Generation for AI-Generated Content: A Survey

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-16T12:10:36.306454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:10:36.306454Z digest=sha256:2c86e269c142bdf19b9ace21f8f323c708b4fc78fd998fd4ec0fa48e58869329

Observation c9f17708-169b-4c19-9918-122e9c364ce5 · outbound

This paper cites Answer Provision.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Answer Provision

Reference 10

Resolution
malformed identifier
raw_fallback, observed 2026-08-16T12:10:36.728298Z

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=pdf_text observed=2026-08-16T12:10:36.314742Z digest=sha256:f6f53d0b2c9928fdc7d8fd3b088897093fc3d25d7906e689678f5a9b640df8cd

Observation 0f01a9c6-c045-49e5-bdd2-c9c5f67bf13d · outbound

This paper cites - Case: The example describes Tom needing to pick up 15 plates from one table and 9 plates from another.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models - Case: The example describes Tom needing to pick up 15 plates from one table and 9 plates from another

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.873749Z

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=pdf_text observed=2026-08-16T12:10:36.338435Z digest=sha256:e4f81d554abe69053682631f199addf668d445690b1e6245e35b06d45df7ba8f

Observation cdb11bdd-a337-443d-9924-4359338f2b2d · outbound

This paper cites - Description: Friend P’s rate is 15% faster than Friend Q’s.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models - Description: Friend P’s rate is 15% faster than Friend Q’s

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.817216Z

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=pdf_text observed=2026-08-16T12:10:36.353758Z digest=sha256:94995fcb8cca1fed8358a0b6eafa21485915b4f186e905b44677793648a4bb07

Observation 52c019fc-0b0a-40b8-a963-e1baf7aa5aa9 · outbound

This paper cites 43 / 2.15v.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models 43 / 2.15v

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.799155Z

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=pdf_text observed=2026-08-16T12:10:36.359277Z digest=sha256:34a22ca6cb21cc99eb4dbbd9a019a9d38d8d3297e10595115536a64ad727f150

Observation d13f2c21-a22d-4ef0-a195-48d6f3d5dfc8 · outbound

This paper cites 23 kilometers.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models 23 kilometers

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.776674Z

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=pdf_text observed=2026-08-16T12:10:36.365122Z digest=sha256:bba422346159d458bc2f27323eba6568e5b4a5fb4b12233a3014b2e0b4a3372c

Observation 2c6d3592-d145-41b8-9448-4a25469dd47b · outbound

This paper cites - Description: James writes a letter to 2 different friends twice a week.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models - Description: James writes a letter to 2 different friends twice a week

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.760480Z

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=pdf_text observed=2026-08-16T12:10:36.369935Z digest=sha256:ccb3544f43b99d9e1a7f7fa89796449f1a7e29bd63a5a308a20b3dbe15599184

Observation 7251acb9-70e8-4bf2-b3a3-d4e46ce8d43f · outbound

This paper cites 4 letters.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models 4 letters

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.740940Z

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=pdf_text observed=2026-08-16T12:10:36.375142Z digest=sha256:bee94296cd2f10c1a79b2a9b598932fd0a312301f7c6a81fdb7f148ab820d6e6

Observation 5f2ec97a-917a-4be2-813b-984a9ab452ac · outbound

This paper cites 12 pages.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models 12 pages

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.723419Z

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=pdf_text observed=2026-08-16T12:10:36.380521Z digest=sha256:020977558f0703e70fd6f30fbbdc5acc1e5583692d0adf52212583131ba78006

Observation 1348f4d4-dcc0-4838-8b70-5b1681353766 · outbound

This paper cites 624 pages.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models 624 pages

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.707754Z

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=pdf_text observed=2026-08-16T12:10:36.385754Z digest=sha256:257da44e570d6c6daa8a37daf0b47a365da0b2c7403b44ae202a98bd4ed6130d

Observation 502cd2cf-9dbe-4d5f-8b7c-b95f1d1310b8 · outbound

This paper cites - Description: Joan found 70 seashells on the beach.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models - Description: Joan found 70 seashells on the beach

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.691848Z

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=pdf_text observed=2026-08-16T12:10:36.391571Z digest=sha256:d1c4e6a9df3610e29d1667d5f5ccf8367bd299d49fac553f1bd996835451dfce

Observation 65f69842-31db-44a0-b874-1a494022695c · outbound

This paper cites - Description: Joan has 27 seashells left.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models - Description: Joan has 27 seashells left

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.674850Z

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=pdf_text observed=2026-08-16T12:10:36.396905Z digest=sha256:f9da2c4a59932a9f3a31e55dff67b6c167dc11999650a2b29467e78081c5f1b9

Observation 83666e70-cacc-41cc-ad01-635ba9213415 · outbound

This paper cites an unresolved cited work.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Unresolved cited work

Reference 26

Resolution
unresolved
raw_fallback, observed 2026-08-16T12:10:37.657627Z

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=pdf_text observed=2026-08-16T12:10:36.401938Z digest=sha256:5f8168c175238c7aaaeb63131bba8f65914d76a4d53ea3c25620151929abc405

Observation 31302211-39e9-4801-87ae-810f7d5a238c · outbound

This paper cites Who was the member of the ’Mother Love Bone’ band who passed away before the release of the album ’Apple’?.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Who was the member of the ’Mother Love Bone’ band who passed away before the release of the album ’Apple’?

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.639046Z

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=pdf_text observed=2026-08-16T12:10:36.407809Z digest=sha256:7142dfb833b595fdcc6bf8c0b06fcae8b11dadb209b77afd79022bf5752b2176

Observation 2ee7ed77-fd8c-4dcd-8269-f33933d2258a · outbound

This paper cites Mother Love Bone.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Mother Love Bone

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.621169Z

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=pdf_text observed=2026-08-16T12:10:36.413056Z digest=sha256:82afe20738a00737d7ce12ea1586895e0348281dca05ba91a8319cc0e6ebc787

Observation 30aef4b6-d700-4094-99f8-29cdeda4b299 · outbound

This paper cites Mother Love Bone.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Mother Love Bone

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.605327Z

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=pdf_text observed=2026-08-16T12:10:36.418177Z digest=sha256:e4cea24faccd3b888efbb6d861de936af8eb8b80f127022bc5b0006f950e6757

Observation 915a1579-ec1a-4144-a64e-50d31d850211 · outbound

This paper cites Mother Love Bone.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Mother Love Bone

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.588275Z

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=pdf_text observed=2026-08-16T12:10:36.423165Z digest=sha256:edc31bed937c4a616e0c5d2c8377e489a9893d78ef071e22c982ea82f5296b8e

Observation fd403014-ce53-44ba-af72-5c36f66c72d0 · outbound

This paper cites Malfunkshun.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Malfunkshun

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.563681Z

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=pdf_text observed=2026-08-16T12:10:36.428146Z digest=sha256:4713a7e2c23b07620c469106f4276e4fae7f6c50dfafb165d86a4c4dda8b550e

Observation 4f26f767-8fcb-4bb4-a9e1-d8ea339f73df · outbound

This paper cites subdivision.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models subdivision

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.543723Z

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=pdf_text observed=2026-08-16T12:10:36.433581Z digest=sha256:e1b449b233193fcdd5083696e91b0a5a5017f4ac14d6fc20d44ccd8f607e738d

Observation 6cbb169f-27d7-4fa6-abdf-858cf915a099 · outbound

This paper cites - Description: The description relies on the answer from sub_question1, which identified the subdivision as the area most likely to have access control facilities and isolation.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models - Description: The description relies on the answer from sub_question1, which identified the subdivision as the area most likely to have access control facilities and isolation

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.525516Z

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=pdf_text observed=2026-08-16T12:10:36.439007Z digest=sha256:c95f40b1dd8b73fbd12e467433010362be7ec912557e8d8662a9a17fe158e54a

Observation f3d76a34-ee52-4e7b-8492-b0bd843e3ee7 · outbound

This paper cites Is this behavior directly related to understanding and answering the teacher’s questions?.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Is this behavior directly related to understanding and answering the teacher’s questions?

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.507787Z

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=pdf_text observed=2026-08-16T12:10:36.444774Z digest=sha256:033663d22132066c92ed4b724c2cb1c0076f5d4c6696fde79448295a71bbbdcd

Observation a2b51e8c-ea09-47fc-aeaa-215601888d7e · outbound

This paper cites ask for a gold star.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models ask for a gold star

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.489300Z

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=pdf_text observed=2026-08-16T12:10:36.450391Z digest=sha256:d3c441c573650b1fcb001a7e8d84297c379f4550cf5a9c00e90342786b580f01

Observation 7b543125-dde2-47f6-ae08-b10b78436d25 · outbound

This paper cites skip her class.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models skip her class

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.472661Z

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=pdf_text observed=2026-08-16T12:10:36.455640Z digest=sha256:0fd852853805906719592d74ff5218800882aa6754b7345cf614ecb85447d49f

Observation e9f31039-b2e6-4021-8e0d-44873395f9c3 · outbound

This paper cites know the information.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models know the information

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.450465Z

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=pdf_text observed=2026-08-16T12:10:36.460816Z digest=sha256:30bbdc8389784f1bdd5bf520e77b47a454397b18e21ade45456b62ee34ccdf74

Observation 5e2448eb-10b9-446c-ba88-d43bc60254bc · outbound

This paper cites No," "No,.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models No," "No,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.433016Z

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=pdf_text observed=2026-08-16T12:10:36.465830Z digest=sha256:963c9b8f2f13a0799c3b003b4f4c5e5dbea1081a5066c232869da6e79b5cf36e

Observation d2003621-5e19-4d7a-91be-dd156c14262b · outbound

This paper cites Pretty" is.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Pretty" is

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.414447Z

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=pdf_text observed=2026-08-16T12:10:36.471354Z digest=sha256:bffcf6228fbc556dca44a60a9909d22efda1fce3aec61a7176d20785e37a2177

Observation 330c76b0-dce3-4d40-8f7d-22c2c2e0a7b5 · outbound

This paper cites Jada" is.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Jada" is

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.396471Z

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=pdf_text observed=2026-08-16T12:10:36.476843Z digest=sha256:0490e0946969d4e3ea0d7d6ce0f0c8c8005e11e2d74dc77246125189e3bbfc04

Observation c52c2231-cc4b-4f3a-b456-6c6186dc6304 · outbound

This paper cites Sarita" is.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Sarita" is

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.379836Z

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=pdf_text observed=2026-08-16T12:10:36.482413Z digest=sha256:cf4c526603719df084308a8e562bcb66536040cebbca5ad19f60bfab897b7a44

Observation 59fa2f0a-3924-4ec0-82c8-9aee6eb39f7f · outbound

This paper cites Allen" is.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Allen" is

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.362071Z

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=pdf_text observed=2026-08-16T12:10:36.487554Z digest=sha256:5fac8f2762af67b87e850f01dae728600142bc04f7006c4b13bc6f78cbff6451

Observation e4008d3b-2f6c-4ce4-a332-6af23b6aadd3 · outbound

This paper cites n" (from Gavin),.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models n" (from Gavin),

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.344982Z

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=pdf_text observed=2026-08-16T12:10:36.492290Z digest=sha256:4f5316fd40bee104998ad2affef439a2bc18d09cf06b02a59d5bcd881694fccc

Observation 6e5c36d2-944a-4f94-9b85-029e7a52ba0b · outbound

This paper cites heads up.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models heads up

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.327899Z

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=pdf_text observed=2026-08-16T12:10:36.498083Z digest=sha256:9fbec3188535e8adeef6471a51e32b30dbba4f5852704b9c59edf9faf34ebf1f

Observation 89eca5b8-5d47-4144-9de2-61e39fc2a355 · outbound

This paper cites heads up.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models heads up

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.310954Z

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=pdf_text observed=2026-08-16T12:10:36.503290Z digest=sha256:36ed50a74d9fd249b944affca089b5d2e43afda7f0c8c780021e9daddc496dc2

Observation ed35f78d-93d9-4cdf-b7e4-1fe790ac4b0c · outbound

This paper cites heads up.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models heads up

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.294409Z

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=pdf_text observed=2026-08-16T12:10:36.508069Z digest=sha256:06fe528cd4df8c7a710eae6e27e8d473644c3cefe99c8d30642d82912b2ce04a

Observation 8f04001a-783b-4c57-90cc-d34b4091234b · outbound

This paper cites tails up.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models tails up

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.266631Z

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=pdf_text observed=2026-08-16T12:10:36.513131Z digest=sha256:0b6f1cc198b07c41f3ce4dca1aa934d1dd029fda7c5ec6afad0d2a60197d23c2

Observation 4fd98416-72d4-4c85-a1c4-952b19ed4862 · outbound

This paper cites What is the amount of cash stolen in this theft?.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models What is the amount of cash stolen in this theft?

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.249815Z

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=pdf_text observed=2026-08-16T12:10:36.517976Z digest=sha256:d5e71f2cde44ed7297ce56672761765e066fa359c3266c96e20dcb7bb72acbee

Observation 0f122e0e-a112-4f9b-8841-8024c61696ab · outbound

This paper cites - Description: The theft took place on January 2, 2016, where Song XX cut open the victim’s coat pocket and stole a small yellow envelope containing 1,500 yuan.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models - Description: The theft took place on January 2, 2016, where Song XX cut open the victim’s coat pocket and stole a small yellow envelope containing 1,500 yuan

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.233349Z

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=pdf_text observed=2026-08-16T12:10:36.523333Z digest=sha256:21016a562b37099df620eec5ae5d49470c94a63b729a040431255ce807d06f40

Observation e8217832-0ed5-4d44-85dd-7509eb63aece · outbound

This paper cites - Description: On January 20, 2016, Song XX stole 7,000 yuan from the victim Zhang Mou 1’s coat pocket.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models - Description: On January 20, 2016, Song XX stole 7,000 yuan from the victim Zhang Mou 1’s coat pocket

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.215123Z

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=pdf_text observed=2026-08-16T12:10:36.534172Z digest=sha256:b4ff23f924265f11b8c78f06fda14145d384ee786d55a96c021ca68464252f55

Observation d035c311-bd21-4b5e-ad21-e0d2b7e6d654 · outbound

This paper cites - Description: The description contains both ’answer1’ (1,500 yuan) and ’answer2’ (7,000 yuan).

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models - Description: The description contains both ’answer1’ (1,500 yuan) and ’answer2’ (7,000 yuan)

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.197519Z

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=pdf_text observed=2026-08-16T12:10:36.540786Z digest=sha256:9eaea095e8d17cf2ef8a2ef48635327aadb8938f6dd6863acebda09d545da021

Observation a9131e94-f7e5-4884-a5d2-ccedfc56d36e · outbound

This paper cites Does the contract mention any content regarding usage permissions?.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Does the contract mention any content regarding usage permissions?

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.181041Z

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=pdf_text observed=2026-08-16T12:10:36.546265Z digest=sha256:81e698d5ce33d105bd3915cadfc038766cafd07cc253937d079782e025e44810

Observation 92fd9a81-f6ce-4af9-b842-a6abe48dee03 · outbound

This paper cites - Description: Roger can carry 4 trays at a time.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models - Description: Roger can carry 4 trays at a time

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.159794Z

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=pdf_text observed=2026-08-16T12:10:36.552243Z digest=sha256:7cf29e1bcbaaf1af9299234b9e3bff87faa368fa081d63f95ab35a12c5e3fc8a

Observation 3b79da02-c276-4e61-9386-e796840ac8ff · outbound

This paper cites - Description: Roger needs to pick up 10 trays from one table.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models - Description: Roger needs to pick up 10 trays from one table

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.134338Z

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=pdf_text observed=2026-08-16T12:10:36.557127Z digest=sha256:5652c56035340ef002c501e77c05725c7cbd4369311405ee788941bcee566fd1

Observation bb69cc6e-7d73-4b01-9c8d-c40b8987c8ee · outbound

This paper cites - Description: Roger needs to pick up 2 trays from another table.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models - Description: Roger needs to pick up 2 trays from another table

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.111255Z

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=pdf_text observed=2026-08-16T12:10:36.562122Z digest=sha256:971f115aa90fc3d8f454ad929f9bb8cd94a888efb442672eb9de20941a5ffed4

Observation 673d5b41-1172-4cef-bf10-ae62689b2e94 · outbound

This paper cites - Description: The total number of trays that Roger needs to carry is the sum of the trays picked up from both tables.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models - Description: The total number of trays that Roger needs to carry is the sum of the trays picked up from both tables

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.088906Z

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=pdf_text observed=2026-08-16T12:10:36.567131Z digest=sha256:03e6e9da36edcc592932ae7a78f8dbdd31c131b53e2f6acad295a0265dbacf82

Observation d214a698-5821-4788-a8a2-27b51f034b9f · outbound

This paper cites How many lunch trays can the person carry at once?.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models How many lunch trays can the person carry at once?

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.068393Z

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=pdf_text observed=2026-08-16T12:10:36.571840Z digest=sha256:a9393a9809febe7ee5eeab0c611988c281691a51687f478638cd268a3744c8b4

Observation bec33d5b-e20c-4af4-b112-c63f4221fcc9 · outbound

This paper cites - Description: Roger can carry 4 trays at a time.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models - Description: Roger can carry 4 trays at a time

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.043838Z

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=pdf_text observed=2026-08-16T12:10:36.576684Z digest=sha256:36fb865c150f04c95aeb2d2a353a74ebecfde13cba8151cbde66a5ecf00e7471

Observation 1990c572-beb7-4950-b76a-cb477a5f75b1 · outbound

This paper cites 10 trays.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models 10 trays

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.024641Z

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=pdf_text observed=2026-08-16T12:10:36.581303Z digest=sha256:4063cbfe7b93fd44c953f5538a38e0e2b51d868a0afc1c28e24709700fef810c

Observation b2963a4e-a982-4954-bec6-1ff092ed7c62 · outbound

This paper cites - Description: Roger needs to pick up 10 trays from one table and 2 trays from another.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models - Description: Roger needs to pick up 10 trays from one table and 2 trays from another

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.002665Z

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=pdf_text observed=2026-08-16T12:10:36.586816Z digest=sha256:12c03c94c227cc78d55009d8a3ac1b392abf49741e4661de9928bb9927dc2c7f

Observation ce9535dc-ae35-4199-b579-0b0dce00c242 · outbound

This paper cites 12 trays.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models 12 trays

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:36.982547Z

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=pdf_text observed=2026-08-16T12:10:36.591466Z digest=sha256:583587db048e12dcdc88eab3433e4776a50d535cb0f6f7ef919e8098803f4af0

Observation c09de3b0-7e50-4ffc-a352-c2b6e25a4808 · outbound

This paper cites If a train travels 300 miles in 5 hours, what is its average speed?.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models If a train travels 300 miles in 5 hours, what is its average speed?

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:36.962764Z

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=pdf_text observed=2026-08-16T12:10:36.595794Z digest=sha256:edf057abce41a7268e05a67219c73ec896f9d2d31c9e99b03e8124cda55c96c2

Observation 1868661f-7089-4d32-8194-eadc9cac61a0 · outbound

This paper cites CFBenchmark: Chinese Financial Assistant Benchmark for Large Language Model.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models CFBenchmark: Chinese Financial Assistant Benchmark for Large Language Model

Reference 2015

Resolution
unresolved
no resolver link, observed 2026-08-16T12:10:36.273239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:10:36.273239Z digest=sha256:7a85ad3c0235ffc48141feb131e13e4d9e9ace169f4131c14ed9418d62c001d6

Observation cac77fe5-0833-4282-b3dd-a759e6fbd8bf · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Training Verifiers to Solve Math Word Problems

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-16T12:10:36.259740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:10:36.259740Z digest=sha256:182df31af29cd0cd48af0bb79583492b3d6fc01fb126aba43f01ff41127dc033

Observation 8b5bd8d4-6024-412f-a6b2-fe459c8d7b36 · outbound

This paper cites How many lunch trays can the person carry at once?.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models How many lunch trays can the person carry at once?

Reference 2022

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.932915Z

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=pdf_text observed=2026-08-16T12:10:36.321577Z digest=sha256:d0dbca9340bf415eb0bdfb53fe5ebf2a67dfedda7fdfdda18e105514c136cc90

Observation 9ed96773-f574-4935-a088-59c4339f56ff · outbound

This paper cites InFindings of the Associa- tion for Computational Linguistics: EMNLP 2023, pages 2550–2575, Singapore.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models InFindings of the Associa- tion for Computational Linguistics: EMNLP 2023, pages 2550–2575, Singapore

Reference 2023

Resolution
verified exact
raw_fallback, observed 2026-08-16T12:10:36.832898Z

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=pdf_text observed=2026-08-16T12:10:36.301055Z digest=sha256:6d3afe5d86bc5051d8e04f28e197063f76ead166cd8be283a0ec85466850cdb8

Observation dde9a114-a261-4c1b-b1a5-6180a2cf6604 · outbound

This paper cites Association for Computational Linguistics.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Association for Computational Linguistics

Reference 2024

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.993129Z

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=pdf_text observed=2026-08-16T12:10:36.252578Z digest=sha256:ff9d641aac2342e4ec18e2b5beb2667b5743c72976e220d106bd9dc9e6d52546

Observation 805d621d-085c-41b6-8d2c-2863bae1b115 · outbound

This paper cites Graph Retrieval-Augmented Generation: A Survey.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Graph Retrieval-Augmented Generation: A Survey

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-16T12:10:36.287259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:10:36.287259Z digest=sha256:a15b1665efffe7a3e24b0aba5cde128f979e17dfab3869dd3f4c786e6e594068

Observation 4b284358-3552-40c3-bd0d-53e1caf6347d · outbound

This paper cites Dongyuan Li, Ying Zhang, Zhen Wang, Shiyin Tan, Satoshi Kosugi, and Manabu Okumura.

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models Dongyuan Li, Ying Zhang, Zhen Wang, Shiyin Tan, Satoshi Kosugi, and Manabu Okumura

Reference 9474

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:10:37.955270Z

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=pdf_text observed=2026-08-16T12:10:36.281293Z digest=sha256:3bd1bcb749203a5e35d15da63710df5887f9b73c73e3eb809f85ffa51f513188

Pith citing papers

Observation ca5a3ca2-47da-4b84-813e-3335d731bfa4 · inbound

Improving the Reliability of LLMs: Combining CoT, RAG, Self-Consistency, and Self-Verification cites this paper.

Improving the Reliability of LLMs: Combining CoT, RAG, Self-Consistency, and Self-Verification CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-15T21:45:32.098095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:45:32.098095Z digest=sha256:501f0755de334b56baafb1e92ce302c58db36e810c5107d632bcd4ecf44b5e41

Observation df82c824-793e-4116-ba9d-9b010e2bf7c5 · inbound

To Isolate or to Score? Model-Adaptive Assessment for Cost-Efficient Multi-Agent RAG cites this paper.

To Isolate or to Score? Model-Adaptive Assessment for Cost-Efficient Multi-Agent RAG CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-07-04T18:40:02.349520Z

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=2026-06-25T22:44:43.951083Z digest=sha256:c1c72c20458244f3bf821400eaaf71f6889dffb9dbfc57ae93c1106dc706d5d9

Observation 781f8aec-9fab-48cf-870d-ea706d776f59 · inbound

Mitigating LLM Sycophancy in Code Smell Detection Using Evidence-Guided Reasoning Prompts cites this paper.

Mitigating LLM Sycophancy in Code Smell Detection Using Evidence-Guided Reasoning Prompts CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-07-14T11:57:56.953101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T11:57:56.953101Z digest=sha256:f874b56db5a15ea89b37178169031a2e00950f0eae797d0748bf48a1de5c8e4e

Observation b4fe00f2-870a-460e-b6f4-9a7b8e16157c · inbound

Requirements-Augmented Generation for Trustworthy Acceptance Testing of LLM-Based Software cites this paper.

Requirements-Augmented Generation for Trustworthy Acceptance Testing of LLM-Based Software CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-15T19:36:49.517909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:36:49.517909Z digest=sha256:5788eceb17a98a95fbccd5b2e29a27f9010bd53dd7db1bac1e92fdb88541b759