Challenge: Harmonized System (HS) code classification is a hierarchically structured and regulation-constrained task, often complicated by short and noisy product descriptions.
Approach: They propose a knowledge-graph-guided LLM framework that formulates HS classification as a stepwise, regulation-aware reasoning process over an explicit HS knowledge graph.
Outcome: The proposed framework constrains inference to legally valid paths while producing explicit and traceable reasoning trajectories.

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Challenge: Existing agent benchmarks neglect hierarchical rule application in real-world domains . a critical gap persists in numerous real-life professional domains where decision-making is governed by expert-written rules.
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Hierarchical Text Classification with LLM-Refined Taxonomies (2026.eacl-long)

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Challenge: Hierarchical text classification (HTC) relies on taxonomies that organize labels into structured hierarchies, but many real-world taxonomies introduce ambiguities, such as identical leaf names under similar parent nodes, which prevent language models from learning clear decision boundaries.
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Challenge: Large language models (LLMs) have remarkable capabilities in understanding complex tasks, but they can only handle graph partitioning tasks that require global perception abilities.
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Graph-Assisted Large Language Models: A Perspective on Mitigating Intrinsic Limitations (2026.findings-acl)

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Challenge: Large language models exhibit intrinsic limitations such as knowledge cutoff, single-threaded reasoning that hinders finer-grained branch and aggregation, and rigid collaboration mechanisms that struggle to coordinate specialized capabilities.
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CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases (2025.naacl-long)

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Challenge: Large Language Models excel in stand-alone code tasks but struggle with handling entire code repositories.
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GALLa: Graph Aligned Large Language Models for Improved Source Code Understanding (2025.acl-long)

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Challenge: Programming languages have rich semantics that are represented by graphs and not available from the surface form of source code.
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InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignment (2024.findings-acl)

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Challenge: Existing large language models (LLMs) can solve graph reasoning and generation tasks with parameter updates without sacrificing performance.
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Enhancing Hierarchical Text Classification through Knowledge Graph Integration (2023.findings-acl)

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Challenge: Existing approaches to hierarchical text classification are limited by lack of domain knowledge, which leads to mistakes in a variety of situations.
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MSG-LLM: A Multi-scale Interactive Framework for Graph-enhanced Large Language Models (2025.coling-main)

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Challenge: Existing graph-enhanced large language models (LLMs) focus on matching subgraphs between subgraph and candidate subgraph at the same scale, neglecting that subgraph with different scales may also share similar semantics or structures.
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GraphEval36K: Benchmarking Coding and Reasoning Capabilities of Large Language Models on Graph Datasets (2025.findings-naacl)

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Challenge: Large language models (LLMs) have demonstrated significant capabilities in processing and understanding text data.
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