Challenge: Existing methods for solving complex problems are expensive and inefficient when handling large-scale, high-complexity problems.
Approach: They propose a multi-agent framework that decomposes complex problems through agent collaboration by mapping implicitly expressed graph data into clear, structured graph representations and dynamically selecting the most suitable algorithm based on problem constraints and graph structure scale.
Outcome: The proposed framework outperforms state-of-the-art methods on multiple benchmarks with robust performance on both closed- and open-source models.

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Challenge: Large language models (LLMs) have revolutionized natural language processing, but their practical implementation as autonomous multi-agent systems remains fraught with unresolved challenges.
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KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph (2025.acl-long)

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Challenge: Existing methods to design the interaction strategy between large language models and knowledge graphs (KGs) are not effective for large language model (LLM)s to solve complex tasks due to the large volume and structured format of KG data.
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Towards Effective and Efficient Multi-Agent Language Model Systems: Foundations, Prospects, and Applications (2026.acl-tutorials)

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Challenge: Multi-agent systems powered by large language models still face challenges . tutorial focuses on three core components to build effective and efficient systems .
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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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LLM-Based Multi-Agent Systems are Scalable Graph Generative Models (2025.findings-acl)

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Challenge: Social graphs are mathematical structures stem from pairwise interactions between entities through nodes and edges.
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Dynamic Generation of Multi LLM Agents Communication Topologies with Graph Diffusion Models (2026.acl-long)

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Challenge: Existing frameworks rely on static or rule-based topologies that fail to adapt to task requirements.
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AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning (2026.acl-long)

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Challenge: Existing agentic frameworks treat external information as unstructured text and fail to leverage topological dependencies inherent in real-world data.
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GraphAgent: Agentic Graph Language Assistant (2025.emnlp-main)

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Challenge: Real-world data combines structured and unstructured formats, capturing explicit relationships and implicit semantic interdependencies.
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Graph Counselor: Adaptive Graph Exploration via Multi-Agent Synergy to Enhance LLM Reasoning (2025.acl-long)

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Challenge: Existing methods for enhancing LLM reliability suffer from inefficient information aggregation and rigid reasoning schemes.
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Planning with Multi-Constraints via Collaborative Language Agents (2025.coling-main)

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Challenge: Recent advances in neural language models have sparked a new surge of intelligent agent research.
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