Title: GraphChain: Large Language Models for Large-scale Graph Analysis via Tool Chaining
Venue: NeurIPS 2025
Accepted: yes
Keywords: Large Language Model

Graph Analysis

Tool Learning
Abstract: Large Language Models (LLMs) face significant limitations when applied to large-scale graphs, struggling with context constraints and inflexible reasoning. We introduce GraphChain, a novel framework enabling LLMs to analyze large graphs by orchestrating dynamic sequences of specialized tools, mimicking human exploratory processes. GraphChain incorporates two core technical contributions: (1) Progressive Graph Distillation, a reinforcement learning approach that learns to generate tool sequences balancing task relevance and intermediate state compression, thereby overcoming LLM context limitations. (2) Structure-aware Test-Time Adaptation (STTA), a mechanism using a lightweight, self-supervised adapter conditioned on graph spectral properties to efficiently adapt a frozen LLM policy to diverse graph structures via soft prompts without retraining. Experiments show GraphChain significantly outperforms prior methods, enabling scalable and adaptive LLM-driven graph analysis.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces a framework named GraphChain to tackle the challenges large language models (LLMs) face in large-scale graph analysis, such as context exhaustion and reasoning hallucination. Concretely, it proposes a dynamic tool-chaining mechanism that mimics human exploratory reasoning. It leverages two core innovations: Progressive Graph Distillation for generating concise and relevant tool sequences, and Structure-aware Test-Time Adaptation for adapting to diverse graph topologies. The experimental results reveal that GraphChain significantly outperform baselines.\n\nFrom my perspective, The paper designs a three-stage graph understanding framework that transforms the usual \"subgraph sampling + direct LLM inference\" paradigm into \"subgraph sampling → Refining → LLM inference.\"\n\nSTRENGTHS AND WEAKNESSES:\n**Pros**\n1. **Progressive Refinement:** By supervising the refine step with PPO, the framework learns to select and chain graph-processing tools that maximize task relevance while minimizing unnecessary information.\n2. **Scalability to Large Graphs:** This pipeline enables LLMs to handle large-scale graphs with both structural and feature information, overcoming context-window limits.\n\n**Cons**\n\n1. **Manual Tool Library:** All subgraph-sampling and processing tools are hand-crafted; an end-to-end mechanism for automated subgraph selection would increase flexibility.\n2. **Structure-Only Evaluation:** Although the method formally incorporates node features and evaluates on multiple domains, all experiments and case studies focus on pure topological reasoning tasks, leaving its effectiveness on feature-rich and true cross-domain graph problems unverified.\n\nQUESTIONS:\n1. Does the author leverage feature information during the training and evaluation process? If GraphChain primarily focuses on graph computational problems, it is recommended that the authors make this explicit in the paper to clarify the intended scope and highlight the method's strengths.\n\nLIMITATIONS:\nYes, but the paper needs to further clarify its ability to process and utilize feature information.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a framework called GraphChain to enhance the capabilities of Large Language Models (LLMs) in processing large-scale graph data. In this paper, the authors propose two key approaches to allow for a more detailed and adaptive exploration of graph structures: Progressive Graph Distillation and Structure-aware Test-Time Adaptation. The framework significantly outperforms existing methods, achieving an average improvement of 20.7% in average. GraphChain demonstrates strong transfer learning capabilities, adapting effectively to new graph domains without domain-specific retraining. Overall, GraphChain provides a scalable and adaptive solution for LLM-driven graph analysis, addressing the challenges of context exhaustion and reasoning hallucination that are prevalent in existing graph processing approaches.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n* The paper presents a well-structured approach to address the limitations of LLMs in graph analysis through the introduction of GraphChain. The use of Progressive Graph Distillation and Structure-aware Test-Time Adaptation is well-justified and supported by theoretical underpinnings.\n* Extensive experiments demonstrate the effectiveness of GraphChain, showing significant improvements over existing methods. The results are statistically significant, with a clear presentation of performance metrics across various datasets.\n\nWeakness:\n* There is a pre-defined set of tools needed, it is unclear how are these tools defined and selected. Will the models be robust to different selections/selections of tools?\n* Compared with previous tool approach, the major improvements come from multiple tool used. If the baselines such as graphsurge are using multiple tools in the reasoning steps, the improvements might be less.\n* The baselines seem to be outdated.\n\nQUESTIONS:\n1. How are the tools be selected? Are models robust to the tool selections?\n2. What would be the performances for text-instruction methods if they are allowed to use these tools?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a framework for large-scale graph analysis using LLMs through dynamic tool chaining. The core idea is to decompose complex graph reasoning tasks into sequential tool-based operations. The method includes two main designs: (1) Progressive Graph Distillation, a reinforcement learning mechanism to generate compact, relevant tool sequences; and (2) Structure-aware Test-Time Adaptation (STTA), which conditions tool selection on spectral fingerprints of the graph to enable efficient adaptation across domains. Experiments show performance gains over baselines in accuracy and scalability.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper proposes a new formulation by combining reinforcement learning and tool-chaining for scalable graph reasoning with LLMs.\n\n2. The design of progressive distillation is conceptually sound, drawing parallels to human exploratory behavior.\n\n3. Experimental results indicate clear improvements over several baselines.\n\n4. The method exhibits promising scalability and transferability across domains, supported by ablations and tool usage analysis.\n\nWeaknesses:\n1. Context exhaustion and reasoning hallucination are not fully resolved. Although GraphChain aims to address these challenges via decomposition, splitting tasks into smaller operations doesn’t necessarily prevent memory overload or hallucination—especially in long reasoning chains. More direct evaluation of these issues would strengthen the claims.\n\n2. The paper lacks justification for how complex graph queries are reliably translated into tool sequences. It’s unclear how stable or generalizable these sequences are, or how sensitive they are to graph variations.\n\n3. Appending spectral graph embeddings to the input is an interesting idea, but it’s unclear if this actually helps the LLM reason better. The mechanism feels ad-hoc, and no ablations or analyses isolate its contribution.\n\n4. Limited details on RL training and policy behavior. The training data is relatively small, and the RL setup—especially reward shaping—is complex. Yet the paper provides little insight into training dynamics, stability, or convergence, making it hard to judge how well the agent learns.\n\n5. Most datasets are small and standard. More realistic and large-scale benchmarks (e.g., from Graph-CoT (https://arxiv.org/abs/2404.07103) or SNAP (https://snap.stanford.edu/data/) ) are missing, and tasks are limited mostly to classification. This restricts the generalizability of results.\n\n6. Only one LLM (Qwen2.5-7B) is evaluated, with no analysis of how model size impacts performance. More recent LLM-based graph reasoning baselines are also missing, which limits the fairness of comparisons.\n\n7. The paper assumes familiarity with the tool library, but doesn’t introduce it clearly upfront. This makes the method harder to understand, especially for readers unfamiliar with specific graph operations.\n\nQUESTIONS:\n1. How stable is the decomposition of graph reasoning into sequential tool calls across different graph types and task complexities? Is there evidence that the sequences are consistent or robust?\n\n2. Can the authors provide more detailed justification or analysis for how the Structure-Conditioned Prompt Generation meaningfully affects the LLM’s reasoning? Have alternative mechanisms been considered?\n\n3. Given the limited size of the expert-annotated RL dataset, how does the agent policy generalize across scenarios? Are there training dynamics, convergence curves, or rollout visualizations to support this?\n\n4. Most results focus on classification tasks. Has GraphChain been tested on other graph reasoning types such as link prediction, subgraph matching, or path-based queries?\n\n5. Why are more recent and larger datasets not included in the evaluation? And why are several recent LLM-based graph reasoning methods not compared?\n\n6. Can the authors more clearly define and categorize the tool library earlier in the paper (e.g., in Preliminaries)? Section 5.6 is informative but arrives late for comprehension.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-05"}

comments: SUMMARY:
This paper introduces GraphChain, which is a method to automatically call graph based API solutions to serve as the intermediate prompt to assist LLM in graph reasoning tasks. To enable the scalability of method to large scale graph, a progressive graph distillation module is proposed to compress graph to overcome the LLM context limitation issue. Besides, a self-supervised adapter is proposed in the test phase.

STRENGTHS AND WEAKNESSES:
Pros.
1. The idea to adopt a reinforcement learning module to automatically select graph solution API and use it as LLM chain of thought prompt is interesting. The proposed progressive graph distillation module alleviates the common LLM issue on limited context length.

2. The writing is clear and easy to follow. Motivations of designed modules are well explained.

3. The experimental parts are solid and comprehensive.

Cons.

1. How the proposed method compare to previous methods that also use graph API for LLM graph reasoning tasks? I understand the progressive graph distillation can alleviate the LLM context limitation issue. My question is besides this point, how the proposed method is different from existing methods?
 
2. The selection pool of graph solution API seems important to proposed method. It would be interesting to understand whether some certain APIs are important for a specific task/domain. 

3. The computational complexity analysis and time cost for training the RL modules may be interesting to discuss and experiment.

QUESTIONS:
See above cons. Besides,

In the introduction parts, the authors mentioned previous LLM methods for graph reasoning are usually suffered from hallucination issue. I am wondering how the proposed method can address/alleviate this issue?

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-09