Author

Xin Wang

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Aug 2026

Curriculum-GraphLLM: Joint Optimization of Architectures, Structures and Texts for Denoised Graph Neural Architecture Search.

Discovering optimal graph neural network (GNN) architectures for various tasks is both labor-intensive and time-consuming. To reduce human effort, graph neural architecture search (GNAS) has recently been utilized to automatically identify effective GNN architectures for specific tasks, achieving competitive or even superior performance compared to manually designed architectures. However, existing GNAS methods fail to identify optimal architectures in the presence of structural and semantic noise, where structural noise refers to missing or redundant edges within the graph structure, and semantic noise denotes inaccurate node representations derived from ambiguous node features such as textual vagueness or semantic ambiguity. In this paper, we address this problem for the first time via theoretical analyses and empirical evaluations. We discover that existing differentiable GNAS methods typically select architectures based on task-relevant information hidden in the graph, being highly sensitive to structural and semantic noise, which results in suboptimal selection of GNN architectures under noise. To handle the structural and semantic noise, we propose Curriculum-GraphLLM, a novel graphLLM framework for joint optimization of architectures, structures, and texts for denoised graph neural architecture search. The core idea is to jointly optimize GNN architectures, graph structures, and textual semantics as a unified denoising process during architecture search. Specifically, we first develop a dynamic topology updating mechanism to adaptively adjust the graph structure. Then, we jointly optimize the GNN architecture and graph structure through a curriculum-based iterative updating approach. To further deal with semantic noise on text-attributed graphs (TAGs), we introduce LLMs as an auxiliary text modeling module to refine textual semantics and guide the co-optimization of text representations, graph structures, and GNN architectures. We conduct extensive experiments to show that our proposed Curriculum-GraphLLM achieves consistently competitive or superior performance compared with existing baselines, especially under structural and semantic noise.

Xin Wang, Haibo Chen, Linxin Xiao et al. · IEEE Transactions on Pattern... · 0 citations

Preprint Jul 2026

Modularized Dynamic-Granularity Video LLM for Multi-Event Long Video Understanding

Video Large Language Models (Video LLMs) have made significant advancements in various video understanding tasks. However, long-video scenarios remain challenging due to the tension between limited visual token budgets and the need to capture multiple key events. Existing approaches typically process long videos in two stages, i.e., i) select keyframes and ii) perform detailed perception, which exhibit limitations: they lack a modular mechanism for adaptive capacity allocation and self-correction, resulting in unreliable modeling. To tackle these challenges, we propose MoD-VLLM, a novel Modularized Dynamic-Granularity Video LLM framework for multi-event long video understanding, which unifies temporal grounding and semantic understanding iteratively and self-reflectively. Specifically, we propose a Positive-Negative Video Segments Grounding module and a Modularized Dynamic-Granularity Reflection module, which form a closed loop to progressively localize the question-related video segments. The grounding module instructs a Video LLM to distinguish relevant from irrelevant video segments based on the video question. The reflection module employs a modularized scheduler that dynamically selects fine-grained encoding for relevant positive segments to capture detailed perception and coarse-grained encoding for negative segments to maintain global context. We further propose a dynamic-granularity reinforcement learning strategy, allowing MoD-VLLM to learn optimal grounding policies and dynamic granularity visual representation jointly. Moreover, we propose MEventBench, a challenging Multi-Event Long Video Benchmark for complex long video reasoning. Extensive experiments on several long video understanding benchmarks and our MEventBench demonstrate that MoD-VLLM significantly outperforms state-of-the-art baselines.

Wei Feng, Xin Wang, Yuwei Zhan et al. · 0 citations