VidLaDA: Bidirectional Diffusion Large Language Models for Efficient Video Understanding

• URL: http://arxiv.org/abs/2601.17868v1
• Date: Sun, 25 Jan 2026 15:02:01 GMT
• Title: VidLaDA: Bidirectional Diffusion Large Language Models for Efficient Video Understanding
• Authors: Zhihao He, Tieyuan Chen, Kangyu Wang, Ziran Qin, Yang Shao, Chaofan Gan, Shijie Li, Zuxuan Wu, Weiyao Lin,
• Abstract summary: VidDA is a Video LLM based on the Diffusion Language Model.<n>We introduce MARS-Cache to tackle the bottleneck inference of diffusion decoding on massive video tokens.<n>Experiments show VidDA outperforms diffusion baselines and rivals state-of-the-art autoregressive models.
• Score: 52.69880888587866
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Standard Autoregressive Video LLMs inevitably suffer from causal masking biases that hinder global spatiotemporal modeling, leading to suboptimal understanding efficiency. We propose VidLaDA, a Video LLM based on Diffusion Language Model utilizing bidirectional attention to capture bidirectional dependencies. To further tackle the inference bottleneck of diffusion decoding on massive video tokens, we introduce MARS-Cache. This framework accelerates inference by combining asynchronous visual cache refreshing with frame-wise chunk attention, effectively pruning redundancy while preserving global connectivity via anchor tokens. Extensive experiments show VidLaDA outperforms diffusion baselines and rivals state-of-the-art autoregressive models (e.g., Qwen2.5-VL and LLaVA-Video), with MARS-Cache delivering over 12x speedup without compromising reasoning accuracy. Code and checkpoints are open-sourced at https://github.com/ziHoHe/VidLaDA.

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