Paper Citation Record · LEDGER

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning

As of 20 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2505.06321.

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

pith.paper-citation-record.v1
2505.06321 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:04:05.841223Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

37 of 37 outbound references displayed

verified exact0
verified fuzzy21
unresolved16
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 851ba6f2-30a6-4cdc-8e93-b944b8513a2f · outbound

This paper cites Graph of thoughts: Solving elaborate problems with large language models.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Graph of thoughts: Solving elaborate problems with large language models

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.393797Z

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Observation 471edefb-d67e-4b8c-a70d-e8c061a4efa6 · outbound

This paper cites an unresolved cited work.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Unresolved cited work

Reference 2

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Observation 372f2d89-ed87-4598-8369-6e89b9c27fd3 · outbound

This paper cites Language models are few-shot learners.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Language models are few-shot learners

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.365756Z

Source-reported events for the cited work

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Observation b2fdf4ef-e223-48e6-b3f2-e891b6808115 · outbound

This paper cites Navigate through enigmatic labyrinth A survey of chain of thought reasoning: Advances, frontiers and future.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Navigate through enigmatic labyrinth A survey of chain of thought reasoning: Advances, frontiers and future

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.351827Z

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Observation 6d87d15c-a203-4f3d-9858-edfaaddf8de0 · outbound

This paper cites Chain-of-verification reduces hallucination in large language models.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Chain-of-verification reduces hallucination in large language models

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.338408Z

Source-reported events for the cited work

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Observation a6829682-aa46-486d-9ace-4f33a8087285 · outbound

This paper cites Active prompting with chain-of-thought for large language models.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Active prompting with chain-of-thought for large language models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.323124Z

Source-reported events for the cited work

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Observation e5ba0f5b-2123-4421-aafa-ac611ff10a40 · outbound

This paper cites Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.308105Z

Source-reported events for the cited work

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Observation 981c98ae-a0d5-4e61-9338-c89c4cf60fda · outbound

This paper cites Recent advances in neural machine translation.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Recent advances in neural machine translation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.292130Z

Source-reported events for the cited work

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Observation 007234d5-457a-4699-8f60-6e52bd42b4f6 · outbound

This paper cites Kipf and Max Welling.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Kipf and Max Welling

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.277684Z

Source-reported events for the cited work

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Observation 225adccd-a3b0-4c0e-8a8b-0c40aa5c6b09 · outbound

This paper cites Konda and John N.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Konda and John N

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.262044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 432cedcb-96ca-44eb-b863-b5c4abbfd699 · outbound

This paper cites u ttler, Mike Lewis, Wen - tau Yih, Tim Rockt \.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning u ttler, Mike Lewis, Wen - tau Yih, Tim Rockt \

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.247330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 25e39925-cb33-4dfc-b12e-3fc870f30891 · outbound

This paper cites Large language models understand and can be enhanced by emotional stimuli, 2023.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Large language models understand and can be enhanced by emotional stimuli, 2023

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.231988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4193cc14-807c-4913-a0c0-d6b2f1a9e8c9 · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Reference 13

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Source-reported events for the cited work

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Observation a6f01200-aff2-4171-9038-b7ee771942de · outbound

This paper cites Large Language Model Guided Tree-of-Thought.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Large Language Model Guided Tree-of-Thought

Reference 14

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Source-reported events for the cited work

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Observation 88d209da-f1ce-4968-a949-24b4ae5180a3 · outbound

This paper cites Directgpt: A direct manipulation interface to interact with large language models.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Directgpt: A direct manipulation interface to interact with large language models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.206193Z

Source-reported events for the cited work

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Observation c4fc99bc-519c-4c87-8fb7-71a04e8845ec · outbound

This paper cites Liar, liar, logical mire: A benchmark for suppositional reasoning in large language models.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Liar, liar, logical mire: A benchmark for suppositional reasoning in large language models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.191554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7bf8e91b-b44f-4d79-b3b4-4ed5c92f5726 · outbound

This paper cites CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T23:04:05.694531Z

Source-reported events for the cited work

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Observation f4f47189-ebff-4924-8a4b-d0a38ad8052a · outbound

This paper cites GPT-4 Technical Report.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning GPT-4 Technical Report

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T23:04:05.701760Z

Source-reported events for the cited work

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Observation af7e62ea-c344-4599-ba3d-2ee6a4647319 · outbound

This paper cites ART: Automatic multi-step reasoning and tool-use for large language models.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning ART: Automatic multi-step reasoning and tool-use for large language models

Reference 19

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Observation 27231659-b8e3-4dba-9a14-6e2b9480ff8a · outbound

This paper cites The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink

Reference 20

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Observation 182fcb0f-e99c-4c49-8b03-8dd83ab1fbfd · outbound

This paper cites Reasoning with language model prompting: A survey.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Reasoning with language model prompting: A survey

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.178203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6ea2a5ce-ad65-4f2c-b599-51ba01b2f2fc · outbound

This paper cites Improving language understanding by generative pre-training.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Improving language understanding by generative pre-training

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.163628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9cb88ba5-b92d-4dfb-9cee-489e1d258719 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Proximal Policy Optimization Algorithms

Reference 23

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Source-reported events for the cited work

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Observation 5d889c66-3168-4ac0-9575-9ff28c34711d · outbound

This paper cites Algorithm of thoughts: Enhancing exploration of ideas in large language models.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Algorithm of thoughts: Enhancing exploration of ideas in large language models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.148673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 35037ca9-cc35-4016-9c8f-31d9bab0cb59 · outbound

This paper cites Black-Box Tuning for Language-Model-as-a-Service.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Black-Box Tuning for Language-Model-as-a-Service

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T23:04:05.789001Z

Source-reported events for the cited work

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Observation 70725625-5b96-4769-a9b7-503581835379 · outbound

This paper cites Le, Ed H.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Le, Ed H

Reference 26

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Source-reported events for the cited work

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Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Chi, Quoc V

Reference 27

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Source-reported events for the cited work

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Observation 2262c70e-5752-4cb4-9ac7-1386764182b0 · outbound

This paper cites A comprehensive survey on graph neural networks.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning A comprehensive survey on graph neural networks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T23:04:05.801006Z

Source-reported events for the cited work

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Observation 11507748-a638-45c3-89dd-873e197b367b · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Tree of thoughts: Deliberate problem solving with large language models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T23:04:05.804890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4c24dce-c1fe-4fdb-bfb4-ee59672058a1 · outbound

This paper cites Narasimhan, and Yuan Cao.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Narasimhan, and Yuan Cao

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.093388Z

Source-reported events for the cited work

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Observation 26434683-87df-4b5e-aedf-407cbd80beda · outbound

This paper cites Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T23:04:05.812514Z

Source-reported events for the cited work

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Observation 0938912c-5231-4f06-98c8-fe43b50a77fa · outbound

This paper cites Automatic chain of thought prompting in large language models.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Automatic chain of thought prompting in large language models

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.078448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9f9a2ecf-2f2c-48a5-b203-b77c03c93c5b · outbound

This paper cites Enhancing zero-shot chain-of-thought reasoning in large language models through logic.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Enhancing zero-shot chain-of-thought reasoning in large language models through logic

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.062720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 99a567cf-fd2d-4a7f-96f1-49437ff17c46 · outbound

This paper cites Learning to prompt for vision-language models.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Learning to prompt for vision-language models

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.046897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c03bfbee-d0e8-4ba3-bc6e-9ae5ef9b3456 · outbound

This paper cites Large language models are human-level prompt engineers.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Large language models are human-level prompt engineers

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:04:06.031095Z

Source-reported events for the cited work

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Observation a6372b50-acf4-4257-b6cf-7feba649e2e0 · outbound

This paper cites Thread of Thought Unraveling Chaotic Contexts.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning Thread of Thought Unraveling Chaotic Contexts

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T23:04:05.836354Z

Source-reported events for the cited work

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Observation 14ef27f6-08b2-40ae-afdd-bb9243a773ad · outbound

This paper cites write newline.

Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning write newline

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T23:04:05.841223Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.