Paper Citation Record · LEDGER

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning

As of 8 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 8 inbound Pith citation observations for arXiv:2505.18499.

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

pith.paper-citation-record.v1
2505.18499 v3

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:34:06.823302Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T22:31:35.760439Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:10:07.125928Z

Reference resolution

56 of 56 outbound references displayed

verified exact1
verified fuzzy32
unresolved22
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f07092ee-8836-493d-a848-d2d54fe2bb8e · outbound

This paper cites Llama3 foundation models.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Llama3 foundation models

Reference 1

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Source-reported events for the cited work

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Observation 20d3740b-fbdc-4bea-a9f7-ea87f695e3f6 · outbound

This paper cites and Albert, R.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning and Albert, R

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:12.043097Z

Source-reported events for the cited work

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Observation 3480f932-177d-4db8-abb0-62d8952913e7 · outbound

This paper cites an unresolved cited work.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Unresolved cited work

Reference 3

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Observation 425e6e5f-48f4-4a58-b102-82c60add58cf · outbound

This paper cites Graphwiz: An instruction-following language model for graph computational problems.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Graphwiz: An instruction-following language model for graph computational problems

Reference 4

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Observation 5cf80da8-02c6-423c-9761-d5c995037c44 · outbound

This paper cites SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training

Reference 5

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Observation dc23e5ef-fd8f-463e-a9b9-91d075459c07 · outbound

This paper cites Graphsos: Graph sampling and order selection to help llms understand graphs better.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Graphsos: Graph sampling and order selection to help llms understand graphs better

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:11.953322Z

Source-reported events for the cited work

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Observation 92b3009a-1c38-4704-a0b5-281e303a02fe · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Training Verifiers to Solve Math Word Problems

Reference 8

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Source-reported events for the cited work

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Observation 01d99bbb-19cf-4f1f-8943-9f4c0a1c72cc · outbound

This paper cites How do large language models understand graph patterns? a benchmark for graph pattern comprehension.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning How do large language models understand graph patterns? a benchmark for graph pattern comprehension

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:11.916308Z

Source-reported events for the cited work

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Observation c2822939-c4f5-4109-8806-a739fded86b6 · outbound

This paper cites Which modality should i use–text, motif, or image?: Understanding graphs with large language models.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Which modality should i use–text, motif, or image?: Understanding graphs with large language models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:11.889285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6883e798-9295-4255-8297-b9d921ae4157 · outbound

This paper cites Erdös-rényi model.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Erdös-rényi model

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:11.862188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 22914956-008d-4a65-b9f6-e8ba5683db7c · outbound

This paper cites Talk like a Graph: Encoding Graphs for Large Language Models.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Talk like a Graph: Encoding Graphs for Large Language Models

Reference 12

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Source-reported events for the cited work

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Observation 12a73d18-f1c4-4487-8cb0-4ed9082d9b89 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 13

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Observation eb795ab9-746e-42c1-bfd2-6f400ae71db7 · outbound

This paper cites A., Schult, D.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning A., Schult, D

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:11.820894Z

Source-reported events for the cited work

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Observation 6993124c-595e-45f8-8eda-1edbbc2d2ccb · outbound

This paper cites Inductive representation learning on large graphs.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Inductive representation learning on large graphs

Reference 15

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Observation 76671d58-c21f-4b16-97ad-065d249fdd70 · outbound

This paper cites Measuring mathematical problem solving with the math dataset.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Measuring mathematical problem solving with the math dataset

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:11.626712Z

Source-reported events for the cited work

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Observation a328fd19-aa29-4f37-a53e-921bd6eb6131 · outbound

This paper cites RULER: What's the Real Context Size of Your Long-Context Language Models?.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning RULER: What's the Real Context Size of Your Long-Context Language Models?

Reference 17

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Observation c05d3363-2e5e-4a6c-bf1e-4beb74182837 · outbound

This paper cites Knowledge graph embedding based question answering.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Knowledge graph embedding based question answering

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:11.486799Z

Source-reported events for the cited work

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Observation 98509042-3b10-4341-91c2-58780ceb6ec3 · outbound

This paper cites and Loukas, A.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning and Loukas, A

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:11.392862Z

Source-reported events for the cited work

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Observation b743cc25-c11d-4dd6-a788-881d6c4a4544 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Semi-Supervised Classification with Graph Convolutional Networks

Reference 20

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Observation 2ec32ea1-cb62-48ef-ba59-9a592b395b10 · outbound

This paper cites Gofa: A generative one-for-all model for joint graph language modeling.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Gofa: A generative one-for-all model for joint graph language modeling

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:11.291272Z

Source-reported events for the cited work

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Observation 68ddfd00-9ab3-487f-bab2-96e7d216e598 · outbound

This paper cites H., Gonzalez, J.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning H., Gonzalez, J

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:11.205988Z

Source-reported events for the cited work

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Observation d2a88161-1b04-46d3-b043-b0560bcc9162 · outbound

This paper cites Can large language models analyze graphs like professionals? a benchmark, datasets and models.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Can large language models analyze graphs like professionals? a benchmark, datasets and models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:11.119760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fd3b1ad9-d1ca-4d57-b2a2-a2d6d004ee21 · outbound

This paper cites Can One Domain Help Others? A Data-Centric Study on Multi-Domain Reasoning via Reinforcement Learning.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Can One Domain Help Others? A Data-Centric Study on Multi-Domain Reasoning via Reinforcement Learning

Reference 24

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Observation 54cf581a-a90f-4d09-bb5f-fb8377d7569a · outbound

This paper cites Let's Verify Step by Step.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Let's Verify Step by Step

Reference 25

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Observation 47d897f3-8c45-4b9e-aef4-ed75d1d1f9b7 · outbound

This paper cites One for all: Towards training one graph model for all classification tasks.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning One for all: Towards training one graph model for all classification tasks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:11.017000Z

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Observation 10bc503f-1111-4db0-8fcd-8c5187cc7cae · outbound

This paper cites Graphinstruct: Empowering large language models with graph understanding and reasoning capability.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Graphinstruct: Empowering large language models with graph understanding and reasoning capability

Reference 27

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Observation 34c69305-9645-4ed6-8e08-067a130d0736 · outbound

This paper cites Position: Graph foundation models are already here.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Position: Graph foundation models are already here

Reference 28

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Observation 012ee3bc-8cba-4bda-8ca4-8e2b6953e2f0 · outbound

This paper cites W., Songhori, E., Wang, S., Lee, Y .-J., Johnson, E., Pathak, O., Nova, A., et al.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning W., Songhori, E., Wang, S., Lee, Y .-J., Johnson, E., Pathak, O., Nova, A., et al

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:10.784236Z

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Observation 68bddacf-9594-4c3a-b823-fe1b493cb600 · outbound

This paper cites OpenAI o1 System Card.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning OpenAI o1 System Card

Reference 30

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Observation 6ccf3fb3-5dd8-48f7-9523-fc31484157cb · outbound

This paper cites Let Your Graph Do the Talking: Encoding Structured Data for LLMs.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Let Your Graph Do the Talking: Encoding Structured Data for LLMs

Reference 31

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This paper cites Qwen2.5 technical report, 2025.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Qwen2.5 technical report, 2025

Reference 32

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Observation 05af27d3-62c9-4c92-b984-e797c1b9f806 · outbound

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G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Unresolved cited work

Reference 33

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Observation a95505f5-fd0a-4df8-9c1e-6e85ef402629 · outbound

This paper cites Understanding transformer reasoning capabilities via graph algorithms.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Understanding transformer reasoning capabilities via graph algorithms

Reference 34

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Observation 8e4ecef4-a11d-45db-9633-a9cfaa882f2e · outbound

This paper cites Approximation ratios of graph neural networks for combinatorial problems.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Approximation ratios of graph neural networks for combinatorial problems

Reference 35

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Observation 83a20312-c9e6-479d-98bf-b59e6f05383f · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 36

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This paper cites Mastering the game of go without human knowledge.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Mastering the game of go without human knowledge

Reference 37

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Observation 07341d31-678d-4e1e-8b9e-42e09d0ae7f2 · outbound

This paper cites Grapharena: Benchmarking large language models on graph computational problems.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Grapharena: Benchmarking large language models on graph computational problems

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:09.887693Z

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Observation c8abb2a4-84d8-42d5-b64f-a870a9d50012 · outbound

This paper cites Qwen2.5: A party of foundation models, September 2024.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Qwen2.5: A party of foundation models, September 2024

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:09.682498Z

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Observation a9b4f9eb-4ae8-4413-9866-25dceec620f8 · outbound

This paper cites Neural execution of graph algorithms.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Neural execution of graph algorithms

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:09.504027Z

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Observation 6b58a7b4-efe1-4f18-b0a8-755419a7f686 · outbound

This paper cites Gcn-rl circuit designer: Transferable transistor sizing with graph neural networks and reinforcement learning.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Gcn-rl circuit designer: Transferable transistor sizing with graph neural networks and reinforcement learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:09.351132Z

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Observation 7a3d8b7c-b0bf-43e0-af40-30132bcf7042 · outbound

This paper cites Can language models solve graph problems in natural language? In NeurIPS, 2023.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Can language models solve graph problems in natural language? In NeurIPS, 2023

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:09.246236Z

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Observation e8136c3c-1cc1-477b-a0c5-4c06d668882e · outbound

This paper cites Instructgraph: Boosting large language models via graph-centric instruction tuning and preference alignment.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Instructgraph: Boosting large language models via graph-centric instruction tuning and preference alignment

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:09.085762Z

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Observation da2c5bd7-8373-4026-9b37-2097cef475e5 · outbound

This paper cites MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Reference 44

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Observation da3e64fa-bb27-48f4-8003-937e5978f3d0 · outbound

This paper cites Exploring graph tasks with pure llms: A comprehensive benchmark and investigation.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Exploring graph tasks with pure llms: A comprehensive benchmark and investigation

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:05.552465Z

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Observation 9e17df82-f29b-430d-b5ba-369428a3a0bd · outbound

This paper cites V ., Zhou, D., et al.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning V ., Zhou, D., et al

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:08.973139Z

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Observation 8486740e-5fff-4e41-b9ed-3801115bfb38 · outbound

This paper cites GraphEval36K: Benchmarking Coding and Reasoning Capabilities of Large Language Models on Graph Datasets.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning GraphEval36K: Benchmarking Coding and Reasoning Capabilities of Large Language Models on Graph Datasets

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:34:07.734783Z

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Observation 349f2148-7748-48a1-9487-846e622bd8ea · outbound

This paper cites When More is Less: Understanding Chain-of-Thought Length in LLMs.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning When More is Less: Understanding Chain-of-Thought Length in LLMs

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:05.839734Z

Source-reported events for the cited work

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Observation 4a69160d-c4d8-4180-8500-83ec50bcb1be · outbound

This paper cites Graphomni: A comprehensive and extendable benchmark framework for large language models on graph-theoretic tasks.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Graphomni: A comprehensive and extendable benchmark framework for large language models on graph-theoretic tasks

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:05.985551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cd9c19c-54aa-4f9b-b258-e9354772360f · outbound

This paper cites How Powerful are Graph Neural Networks?.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning How Powerful are Graph Neural Networks?

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:06.094518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f730e4f1-1428-46e4-bd2c-47f581516e25 · outbound

This paper cites How powerful are graph neural networks? In ICLR, 2019.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning How powerful are graph neural networks? In ICLR, 2019

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:08.802563Z

Source-reported events for the cited work

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Observation a2e5e071-65b0-4489-a9b0-9a3feb2804ff · outbound

This paper cites What Can Neural Networks Reason About?.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning What Can Neural Networks Reason About?

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:06.300801Z

Source-reported events for the cited work

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Observation a94f9e6b-3b21-4632-9414-be6b6ce12932 · outbound

This paper cites Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:06.421005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2da3a5b-f7d1-4a5d-bc4c-0c8785091264 · outbound

This paper cites Language is all a graph needs.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Language is all a graph needs

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:08.666326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e7b84dec-e8ac-43d7-927f-eb16a26287fa · outbound

This paper cites Scaling Relationship on Learning Mathematical Reasoning with Large Language Models.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Scaling Relationship on Learning Mathematical Reasoning with Large Language Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:06.598510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd67ffaa-9653-485c-aa34-1fc5ac323603 · outbound

This paper cites Gracore: Benchmarking graph comprehension and complex reasoning in large language models.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Gracore: Benchmarking graph comprehension and complex reasoning in large language models

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:34:08.542940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 88267654-a48c-420d-a84b-cdd55ac9392d · outbound

This paper cites c", C, N); dot(.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning c", C, N); dot(

Reference 57

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T14:34:07.227699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation 4daadc44-2853-4936-9e15-42ff53d97661 · inbound

Lost in Serialization: Invariance and Generalization of LLM Graph Reasoners cites this paper.

Lost in Serialization: Invariance and Generalization of LLM Graph Reasoners G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T22:31:35.760439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 024d6d09-6cdc-41cd-b192-06df47f2e0d8 · inbound

GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning cites this paper.

GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-15T18:50:16.995729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 744a6f0c-14fd-43e9-b57a-926c091d0258 · inbound

Position: How can Graphs Help Large Language Models? cites this paper.

Position: How can Graphs Help Large Language Models? G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning

Reference 80

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:10:43.010539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0db60933-fbe5-42bb-bbec-588822b1f1ae · inbound

Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key cites this paper.

Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:21:08.308332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3081c12a-72cc-4024-b133-a297a042a9af · inbound

Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key cites this paper.

Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:21:19.190414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5c0c03e4-3473-4c82-9361-a24de35999fd · inbound

Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key cites this paper.

Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:39:10.291987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8e53f83e-641f-4c70-a080-788c4e32c125 · inbound

Are Large Language Models Suitable for Graph Computation? Progress and Prospects cites this paper.

Are Large Language Models Suitable for Graph Computation? Progress and Prospects G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning

Reference 184

Resolution: verified exact; arxiv_id, observed 2026-07-02T17:07:12.300652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6e23983a-588f-448e-b702-c8b5965ce3a0 · inbound

TheoremGraph: Bridging Formal and Informal Mathematics cites this paper.

TheoremGraph: Bridging Formal and Informal Mathematics G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-04T20:10:07.127633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.