AI模型也能接力跑：谁更靠谱谁先上
大模型生成答案时，不同模型在不同阶段各有优劣。研究者发现，模型在生成过程中对答案的信心变化有规律：靠谱的生成路径上，信心稳定；不靠谱的则波动。基于此，他们设计了一个“接力”框架：让多个模型同时生成，实时追踪每个模型对关键位置的信心，谁当前更靠谱，就把半成品传给谁继续生成。在多个推理任务上，这种接力方式比单个模型或简单投票效果更好。它不是你明天能用上的，但揭示了模型协作的新思路——不是拼凑结果，而是动态选择生成路径。
📄 原文摘要(英文)
Masked Diffusion Language Models (MDLMs) have emerged as a distinct paradigm for sequence generation. As MDLMs become diverse in capabilities and knowledge coverage, an important question is how to combine their knowledge. Toward this, we first investigate the unique decoding dynamics of MDLMs. We find that successful generations exhibit stable confidence dynamics over answer-relevant positions, while unreliable trajectories can often be corrected by injecting promising intermediate states from other models. Guided by this observation, we propose TIE (Trajectory-based Iterative Ensembling), a knowledge fusion framework in which MDLMs iteratively identify reliable decoding trajectories and relay them across models. TIE tracks confidence dynamics over answer-relevant positions to determine which model currently follows a more reliable trajectory and selectively transfers partially denoised sequences across models. As the model on the more promising trajectory often changes across denoising steps, TIE allows different models to contribute complementary strengths at different stages of generation. Strong performance across diverse reasoning tasks, along with our analyses, suggests that TIE offers a practical approach to the underexplored problem of MDLM ensembling.