ST-Veto：通过泰勒预测和视觉基础实现用于扩散多模态语言模型的时空令牌否决
ST-Veto: Spatio-Temporal Token Veto for Diffusion MLLMs via Taylor Prediction and Visual Grounding
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中文总结 AI 辅助
研究针对扩散多模态大语言模型推理不足问题，提出无需训练的ST-Veto方法，利用二阶泰勒预测和图像注意力质量否决不稳定及弱基础令牌，与更安全候选交换，在多基准测试中优于其他方法，提升准确率且无额外成本。
中文摘要 AI 辅助
视觉语言模型（VLMs）通过思维链（CoT）提示实现了强大的推理能力，但存在顺序生成成本高、错误累积和自我纠正能力有限等问题。扩散多模态大语言模型（dMLLMs）在一个与顺序无关的过程中解蔽令牌，提高了效率并实现了迭代优化，但其推理及如何增强推理仍未得到充分探索。我们提出了一种无需训练的方法——时空令牌否决（ST-Veto），它利用在每个扩散步骤观察所有令牌位置的能力。ST-Veto通过置信度动态的二阶泰勒预测否决时间上不稳定的令牌，并使用图像注意力质量过滤弱基础令牌，将它们与更安全的候选令牌交换。在多个dMLLMs和多模态推理基准上，ST-Veto始终优于标准解码策略和先前的VLM推理方法，在不增加额外训练或生成成本的情况下，准确率提高了9%。分析表明，ST-Veto将生成引导向更高置信度、更好基础的路径。
英文摘要
Vision Language Models (VLMs) achieve strong reasoning with Chain-of-Thought (CoT) prompting but incur high sequential-generation cost, error accumulation, and limited self-correction. Diffusion Multimodal Large Language Models (dMLLMs) unmask tokens in an order-agnostic process, improving efficiency and enabling iterative refinement, yet their reasoning and how to enhance it remain underexplored. We propose a training-free method, Spatio-Temporal Token Veto (ST-Veto), which leverages the ability to observe all token positions at each diffusion step. Rather than relying only on current-step confidence, ST-Veto vetoes temporally unstable tokens via second-order Taylor prediction of confidence dynamics and filters weakly grounded tokens using image-attention mass, swapping them with safer candidates. Across multiple dMLLMs and multimodal reasoning benchmarks, ST-Veto consistently outperforms standard decoding policies and prior VLM reasoning methods, improving accuracy by up to 9% with no additional training or generation cost. Analyses show that ST-Veto steers generation toward higher-confidence, better-grounded paths.
