Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:34:06.823302Z
Paper Citation Record · LEDGER
G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning
As of 17 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T22:31:35.760439Z
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:34:12.076941Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T14:34:12.016708Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:34:11.988928Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:34:01.648932Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:34:02.027496Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:34:02.124244Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:34:11.760745Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:34:02.541879Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:34:02.854379Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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 2026-08-16T06:30:59.297886+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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:34:03.334852Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:34:03.465024Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:34:03.702868Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:34:10.896471Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:34:03.988206Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:34:04.112727Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a5a756c2-d8e9-4160-8c9b-bef89514d086 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:34:10.633675Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 05af27d3-62c9-4c92-b984-e797c1b9f806 · outbound
This paper cites an unresolved cited work.
G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Unresolved cited work
Reference 33
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T14:34:10.479242Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:34:10.334438Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:34:10.191935Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c4ba0af7-4319-4bc2-a532-76b945c9957c · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:34:10.076130Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:34:05.426545Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Unavailable: canonical work link unavailable.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Unavailable: canonical work link unavailable.
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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+00:00.