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Jun-Gang Li

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Preprint Oct 2026

VTR-Bench: A Systematic Benchmark for Evaluating Visual Text Rendering in Video Generation

Recent video generation models can produce highly realistic videos from natural language instructions, with visual quality approaching cinematic standards. Existing evaluation benchmarks, however, predominantly assess visual quality, aesthetic appeal and physical plausibility, while paying limited attention to text, an...

Yu Huang, Jun-Gang Li, Zhiyuan Wang et al. · 0 citations
#artificial intelligence Preprint Sep 2026

InterTab: Interleaved Visual-Structure Alignment for Multi-Modal Table Reasoning

InterTab is a structure-aware framework for CoT reasoning over table images that interleaves chain-of-thought with tool calls that crop structure-aligned table regions, and improves the average accuracy of its backbone from 68.28% to 73.17% and achieves the best average performance among all compared methods.

Hanqian Li, Si-Rui Huang, Chen Ling et al. · 0 citations
#machine learning Preprint Sep 2026

Vidu S2: Real-Time Interactive, Editable, and Spatial Video Generation

Vidu S2, which comprises Vidu S2-Avatar, a real-time interactive digital-character model, and Vidu S2-Editing, a real-time video editing model, is presented and the feasibility of real-time spatial video generation for both Vidu S2-Avatar and Vidu S2-Editing is explored.

Jintao Zhang, Kai Jiang, Jin-Tao Chen et al. · 3 citations

Unveiling Language Routing Isolation in Multilingual MoE Models for Interpretable Subnetwork Adaptation

A systematic analysis of expert routing patterns in MoE models reveals Language Routing Isolation, in which high- and low-resource languages tend to activate largely disjoint expert sets, and proposes RISE, a framework that exploits routing isolation to identify and adapt language-specific expert subnetworks.

Kening Zheng, Wei-Chieh Huang, Jiahao Huo et al. · 4 citations · ⚡2

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