Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment | Paper Citation Record

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This paper cites https://www.anthropic.com/news/claude-3-7-sonnet.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment https://www.anthropic.com/news/claude-3-7-sonnet

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This paper cites https://deepmind.google/technologies/gemini/pro/.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment https://deepmind.google/technologies/gemini/pro/

Reference 2

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This paper cites https://openai.com/index/o3-o4-mini-system-card/.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment https://openai.com/index/o3-o4-mini-system-card/

Reference 3

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This paper cites On-policy distillation of language models: Learning from self-generated mistakes.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment On-policy distillation of language models: Learning from self-generated mistakes

Reference 4

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This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 5

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This paper cites Language models are few-shot learners.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Language models are few-shot learners

Reference 6

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

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Graphwiz: An instruction-following language model for graph computational problems

Reference 7

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This paper cites LL a GA : Large language and graph assistant.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment LL a GA : Large language and graph assistant

Reference 8

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This paper cites Reasoning Models Don't Always Say What They Think.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Reasoning Models Don't Always Say What They Think

Reference 9

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This paper cites Exploring the potential of large language models (llms) in learning on graphs.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Exploring the potential of large language models (llms) in learning on graphs

Reference 10

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This paper cites Lota-bench: Benchmarking language-oriented task planners for embodied agents.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Lota-bench: Benchmarking language-oriented task planners for embodied agents

Reference 11

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

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training

Reference 12

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This paper cites Training Verifiers to Solve Math Word Problems.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Training Verifiers to Solve Math Word Problems

Reference 13

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Observation 1d3b3a4e-664c-4e34-b349-30dd7bd98384 · outbound

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

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Which modality should I use - text, motif, or image? : Understanding graphs with large language models

Reference 14

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Observation 33dcfe75-57b4-41c7-b0e4-a276dd65ecba · outbound

This paper cites Benchmarks for automated commonsense reasoning: A survey.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Benchmarks for automated commonsense reasoning: A survey

Reference 15

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This paper cites Graphvis: Boosting llms with visual knowledge graph integration.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Graphvis: Boosting llms with visual knowledge graph integration

Reference 16

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This paper cites Knowledge crosswords: Geometric knowledge reasoning with large language models.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Knowledge crosswords: Geometric knowledge reasoning with large language models

Reference 17

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Observation f7da5293-3bd3-4b8d-a902-14277921f4d0 · outbound

This paper cites KTO: Model Alignment as Prospect Theoretic Optimization.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment KTO: Model Alignment as Prospect Theoretic Optimization

Reference 18

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This paper cites Talk like a graph: Encoding graphs for large language models.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Talk like a graph: Encoding graphs for large language models

Reference 19

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This paper cites Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies

Reference 20

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This paper cites Bigbench: Towards an industry standard benchmark for big data analytics.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Bigbench: Towards an industry standard benchmark for big data analytics

Reference 21

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This paper cites The Llama 3 Herd of Models.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment The Llama 3 Herd of Models

Reference 22

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

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 23

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Observation 11484dac-1c3a-4667-965e-39c62904883d · outbound

This paper cites Seed1.5-VL Technical Report.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Seed1.5-VL Technical Report

Reference 24

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This paper cites GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 25

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This paper cites Direct Language Model Alignment from Online AI Feedback.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Direct Language Model Alignment from Online AI Feedback

Reference 26

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This paper cites Hagberg, Daniel A.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Hagberg, Daniel A

Reference 27

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This paper cites P i V e: Prompting with iterative verification improving graph-based generative capability of LLM s.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment P i V e: Prompting with iterative verification improving graph-based generative capability of LLM s

Reference 28

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This paper cites Harnessing explanations: LLM -to- LM interpreter for enhanced text-attributed graph representation learning.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Harnessing explanations: LLM -to- LM interpreter for enhanced text-attributed graph representation learning

Reference 29

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This paper cites G-retriever: Retrieval-augmented generation for textual graph understanding and question answering.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment G-retriever: Retrieval-augmented generation for textual graph understanding and question answering

Reference 30

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

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Measuring mathematical problem solving with the MATH dataset

Reference 31

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This paper cites Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps

Reference 32

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This paper cites REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization

Reference 33

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This paper cites Efficient Test-Time Scaling via Self-Calibration.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Efficient Test-Time Scaling via Self-Calibration

Reference 34

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Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment GPT-4o System Card

Reference 35

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Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment OpenAI o1 System Card

Reference 36

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Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Multi-Turn Code Generation Through Single-Step Rewards

Reference 37

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Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Mistral 7B

Reference 38

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This paper cites SWE-bench: Can Language Models Resolve Real-World GitHub Issues?.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Reference 39

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This paper cites Training Language Models to Self-Correct via Reinforcement Learning.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Training Language Models to Self-Correct via Reinforcement Learning

Reference 40

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This paper cites Rlaif: Scaling reinforcement learning from human feedback with ai feedback.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Rlaif: Scaling reinforcement learning from human feedback with ai feedback

Reference 41

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Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Common 7B Language Models Already Possess Strong Math Capabilities

Reference 42

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This paper cites S*: Test Time Scaling for Code Generation.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment S*: Test Time Scaling for Code Generation

Reference 43

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This paper cites Visiongraph: leveraging large multimodal models for graph theory problems in visual context.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Visiongraph: leveraging large multimodal models for graph theory problems in visual context

Reference 44

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Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Let's verify step by step

Reference 45

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Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Cohn, and Janet B

Reference 46

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This paper cites DeepSeek-V3 Technical Report.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment DeepSeek-V3 Technical Report

Reference 47

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Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Agentbench: Evaluating LLM s as agents

Reference 48

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This paper cites Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct

Reference 49

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Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Reasoning on graphs: Faithful and interpretable large language model reasoning

Reference 50

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Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Language models of code are few-shot commonsense learners

Reference 51

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Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Multi-hop question answering

Reference 52

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Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Sim PO : Simple preference optimization with a reference-free reward

Reference 53

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Observation c09cd8db-81f3-4899-a157-0a5a4213c5b5 · outbound

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Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment s1: Simple test-time scaling

Reference 54

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This paper cites Training language models to follow instructions with human feedback.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Training language models to follow instructions with human feedback

Reference 55

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This paper cites Teach: Task-driven embodied agents that chat.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Teach: Task-driven embodied agents that chat

Reference 56

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Observation eb477d62-1f18-487a-9d1a-5cfef0d628a5 · outbound

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

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Let Your Graph Do the Talking: Encoding Structured Data for LLMs

Reference 57

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This paper cites Introducing gemini 2.0: Our new ai model for the agentic era, Dec 2024.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Introducing gemini 2.0: Our new ai model for the agentic era, Dec 2024

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:52.312428Z

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Observation cb23a208-8f20-4c44-afa0-73abc1de32fb · outbound

This paper cites PHYBench: Holistic Evaluation of Physical Perception and Reasoning in Large Language Models.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment PHYBench: Holistic Evaluation of Physical Perception and Reasoning in Large Language Models

Reference 59

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Observation 56eb9828-edeb-4f0e-af06-3298d770fd12 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Direct preference optimization: Your language model is secretly a reward model

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:44.811893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b6b2cad-3767-4e73-9981-da4ba7c71a92 · outbound

This paper cites Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:44.872410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 488112b6-4cd3-4091-85a3-c93c6afce296 · outbound

This paper cites Explagraphs: An explanation graph generation task for structured commonsense reasoning.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Explagraphs: An explanation graph generation task for structured commonsense reasoning

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:52.158934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 117158e8-d2a4-4b82-acad-a57f262f86c2 · outbound

This paper cites Proscript: Partially ordered scripts generation.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Proscript: Partially ordered scripts generation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:52.003926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0f8f4947-48e1-4209-b02e-f81f09a3c432 · outbound

This paper cites Trust region policy optimization.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Trust region policy optimization

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:45.089426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19312d1e-b80b-448a-8ad4-4ac4ad4a3b33 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Proximal Policy Optimization Algorithms

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:45.150608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55ac6afc-efc0-4230-bf21-ea854c17561c · outbound

This paper cites Scaling Test-Time Compute Without Verification or RL is Suboptimal.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Scaling Test-Time Compute Without Verification or RL is Suboptimal

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:45.177987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07e7de3c-ed3c-4fbb-952f-6138524d4d8a · outbound

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

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:45.228703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18f0b978-ba67-4db2-a50c-a624422e4013 · outbound

This paper cites Dast: Difficulty-adaptive slow-thinking for large reasoning models.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Dast: Difficulty-adaptive slow-thinking for large reasoning models

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:45.324656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f4eb02f-7174-4345-b1ac-a2fc735ef968 · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment HybridFlow: A Flexible and Efficient RLHF Framework

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:45.419379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b41a456-27a2-4afa-ad57-8e480850457b · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:45.488258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61a14c50-91e9-45de-9af1-cc587d7f4c41 · outbound

This paper cites Deductive additivity for planning of natural language proofs.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Deductive additivity for planning of natural language proofs

Reference 71

Resolution: verified exact; doi, observed 2026-08-07T12:02:48.902660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 51686ea3-489d-4669-a2b8-a78cae193a02 · outbound

This paper cites Learning to summarize from human feedback.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Learning to summarize from human feedback

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:45.698793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3033efb2-2d8f-4f37-92fb-70b588e97c84 · outbound

This paper cites Table meets llm: Can large language models understand structured table data? a benchmark and empirical study.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Table meets llm: Can large language models understand structured table data? a benchmark and empirical study

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:51.874437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 52744ef0-18a2-4e66-b457-9a8b63e35fb6 · outbound

This paper cites Commonsense QA 2.0: Exposing the limits of AI through gamification.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Commonsense QA 2.0: Exposing the limits of AI through gamification

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:51.568323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f0d1d8e5-ef9a-4e91-9e2d-17e14deefa75 · outbound

This paper cites Graphgpt: Graph instruction tuning for large language models.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Graphgpt: Graph instruction tuning for large language models

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:51.395148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 18b06361-930a-484c-bbea-b79375163e70 · outbound

This paper cites Grapharena: Evaluating and exploring large language models on graph computation.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Grapharena: Evaluating and exploring large language models on graph computation

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:51.185959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e41c1665-69e7-46c6-821e-82833ad0a001 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Gemma: Open Models Based on Gemini Research and Technology

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:46.078869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f653fc66-986b-4e1b-8464-4064198d7143 · outbound

This paper cites Dart-math: Difficulty-aware rejection tuning for mathematical problem-solving.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Dart-math: Difficulty-aware rejection tuning for mathematical problem-solving

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:46.140093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2353a9b1-9598-49d0-98c7-1bf6d4a85c5f · outbound

This paper cites Solving math word problems with process- and outcome-based feedback.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Solving math word problems with process- and outcome-based feedback

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:46.242901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cfa9ee6-9010-4d81-80dc-0e3b6a62e8d1 · outbound

This paper cites Planbench: An extensible benchmark for evaluating large language models on planning and reasoning about change.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Planbench: An extensible benchmark for evaluating large language models on planning and reasoning about change

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:51.054519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cab17d44-cddc-49b2-b162-d227fb4b241a · outbound

This paper cites Trl: Transformer reinforcement learning.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Trl: Transformer reinforcement learning

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:46.408784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d278164a-83a7-47e6-bdd4-399b7cb356d2 · outbound

This paper cites ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:46.521384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f68190f-68da-475a-bcfa-b6810b8f8717 · outbound

This paper cites Llms as zero-shot graph learners: Alignment of gnn representations with llm token embeddings.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Llms as zero-shot graph learners: Alignment of gnn representations with llm token embeddings

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:50.846643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 30cef527-dcd8-4a09-9558-ab22be4a669a · outbound

This paper cites Can language models solve graph problems in natural language? In Thirty-seventh Conference on Neural Information Processing Systems, 2023.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Can language models solve graph problems in natural language? In Thirty-seventh Conference on Neural Information Processing Systems, 2023

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:46.650583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbc50788-a23e-4b39-ba6e-2a62dde2af02 · outbound

This paper cites I nstruct G raph: Boosting large language models via graph-centric instruction tuning and preference alignment.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment I nstruct G raph: Boosting large language models via graph-centric instruction tuning and preference alignment

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:46.729932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation feecfe1d-f5d2-4e96-a23f-937fdb0d76f6 · outbound

This paper cites SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:46.810689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0fb58a5-aad7-4f38-a33d-180219151861 · outbound

This paper cites Codeplan: Unlocking reasoning potential in large language models by scaling code-form planning.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Codeplan: Unlocking reasoning potential in large language models by scaling code-form planning

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:50.725063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation acf3de7f-76a1-4643-9f38-585e08c9ec61 · outbound

This paper cites Can graph learning improve planning in llm-based agents? In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Can graph learning improve planning in llm-based agents? In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:50.578498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7767963a-1620-4454-b979-593879e8d237 · outbound

This paper cites DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:47.047930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16bc7cef-817b-40ec-8be3-2eec16a6f1d0 · outbound

This paper cites TRIGO : Benchmarking formal mathematical proof reduction for generative language models.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment TRIGO : Benchmarking formal mathematical proof reduction for generative language models

Reference 90

Resolution: verified exact; doi, observed 2026-08-07T12:02:50.430587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 19c53e63-1d8d-49b8-bcff-87bf899cb648 · outbound

This paper cites Watch every step! LLM agent learning via iterative step-level process refinement.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Watch every step! LLM agent learning via iterative step-level process refinement

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:47.208149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b197725f-2f5e-4a2c-a2c4-b166dcaef12e · outbound

This paper cites Contrastive preference optimization: Pushing the boundaries of LLM performance in machine translation.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Contrastive preference optimization: Pushing the boundaries of LLM performance in machine translation

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:50.269168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a07df27f-0641-4445-b1c3-bf5ee97d8b17 · outbound

This paper cites Qwen2.5 Technical Report.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Qwen2.5 Technical Report

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:47.429349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbd61906-62d7-4051-a036-23900ad0d07b · outbound

This paper cites EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:47.492691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9af23f9f-4d9b-47fb-8a1b-8f4bbf3eea20 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:47.598747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddd56d78-0fac-4c66-8bcc-01143c2e5400 · outbound

This paper cites Advancing LLM reasoning generalists with preference trees.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Advancing LLM reasoning generalists with preference trees

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:50.114514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 32d60b21-8cf9-41d5-8188-815c95221139 · outbound

This paper cites VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:47.802104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5139680d-f987-4cb5-9adf-875d71ac9ce8 · outbound

This paper cites Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:47.920328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee6b9cf4-c1ce-48e8-8dcc-862e24770cd8 · outbound

This paper cites Chain of preference optimization: Improving chain-of-thought reasoning in llms.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Chain of preference optimization: Improving chain-of-thought reasoning in llms

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:49.881664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 65db2cf2-208c-4ba9-9381-c15e4a99d016 · outbound

This paper cites an unresolved cited work.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Unresolved cited work

Reference 100

Resolution: verified exact; doi, observed 2026-08-07T12:02:48.696972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers