Multimodal fake news proliferates on social media, yet its verification is hindered by context sparsity and analytical heterogeneity: deceptive posts often hinge on implicit social background, while distinct misinformation patterns demand different verification strategies. Existing methods rely on either representation-driven classifiers or LLM-based retrieval-augmented and tool-using pipelines, which act as opaque black boxes or follow rigid, predefined tool chains, failing to adapt reasoning to diverse news and yielding weakly grounded, uninterpretable decisions. To this end, we propose MCTL, a Meta-Cognitive Tool-Learning Framework that reformulates detection from static classification into in-context cognitive orchestration with dynamically planned tool-based reasoning. Specifically, MCTL retrieves cross-modal contextual evidence as external memory to compensate for context sparsity, performs meta-cognitive planning to construct a sample-specific Directed Acyclic Graph (DAG) reasoning blueprint, routes specialized tools along the DAG, and fuses conflicting judgments via consensus-aware arbitration, enabling context-grounded, sample-adaptive, and traceable verification. Extensive experiments demonstrate that MCTL consistently outperforms recent state-of-the-art agentic and RAG baselines, confirming its effectiveness and reliability.

Large language models (LLMs) have made progress in knowledge-intensive tasks, reasoning and planning, and collaborative problem solving, yet they 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. Graphs, with their ability to represent relational knowledge and complex dependencies, offer a natural means to address these limitations: they provide structured, high-density knowledge for augmenting or correcting LLMs’ generation; enable revisitable inference by organizing intermediate steps as graphs; and support dynamic coordination among experts or agents in collaborative settings. Motivated by these developments, we present the first systematic survey of graph-assisted LLMs from the perspective of how graph structures mitigate LLMs’ limitations. We introduce a taxonomy spanning Graph-Assisted Knowledge Augmentation, Graph-Assisted Reasoning and Planning, and Graph-Assisted LLM Collaboration, and analyze representative methods, summarize common design patterns, and outline open challenges and future directions for advancing LLMs with graph-based enhancements. The collected papers are available in link here.