Less is More: Learning Graph Tasks with Just LLMs
On the map: LLM reasoning → Knowledge- and tool-augmented reasoning
Abstract (arXiv)
For large language models (LLMs), reasoning over graphs could help solve many problems. Prior work has tried to improve LLM graph reasoning by examining how best to serialize graphs as text and by combining GNNs and LLMs. However, the merits of such approaches remain unclear, so we empirically answer the following research questions: (1) Can LLMs learn to solve fundamental graph tasks without specialized graph encoding models?, (2) Can LLMs generalize learned solutions to unseen graph structures or tasks?, and (3) What are the merits of competing approaches to learn graph tasks? We show that even small LLMs can learn to solve graph tasks by training them with instructive chain-of-thought solutions, and this training generalizes, without specialized graph encoders, to new tasks and graph structures.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. First posted on arXiv 13 Aug 2025.
Builds on
0 of 0 references.
No references here yet.
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation
- 2024Can LLM Graph Reasoning Generalize beyond Pattern Memorization?
- 2026Are Large Language Models Suitable for Graph Computation? Progress and Prospects
- 2024Can LLMs perform structured graph reasoning?
- 2025Rethinking and Benchmarking Large Language Models for Graph Reasoning
- 2024CodeGraph: Enhancing Graph Reasoning of LLMs with Code
- 2025Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning
- 2024Graph Reasoning with Large Language Models via Pseudo-code Prompting