MoTE模型在多任务视频语言学习方面提供了可解释且计算高效的解码器替代方案，值得一看。
MoTE（混合任务专家）是一种解码器架构，将大型语言模型的正向网络转换为特定任务的专家，同时保持多模态骨干共享。在五个COIN基准上评估，该模型在VideoLLM-MoTE上实现了比最近的VideoLLM基线更高的平均top-1准确率。
MoTE: Mixture of Task Experts for Multi-Task Video Understanding
Procedural video-language models must solve heterogeneous tasks from the same visual evidence, including action recognition, forecasting, and procedure prediction. Dense transformer decoders share the same feed-forward networks across tasks, which can entangle task behavior and make controlled capability expansion difficult. Sparse Mixture-of-Experts (MoE) decoders provide conditional computation, but token-level learned routing is not naturally aligned with task-level procedural objectives. We propose MoTE (Mixture of Task Experts), a decoder architecture that converts large language model feed-forward networks into task-specific experts while keeping the multimodal backbone shared. Each example follows one sample-level task route, so active task-expert computation remains independent of the number of stored task experts. We instantiate this design as VideoLLM-MoTE and evaluate it on five COIN benchmarks using explicit task routes. The five-expert model activates ~2B LLM parameters per sample and achieves higher average top-1 accuracy than recent VideoLLM baselines. Under the same expert topology, it improves over dense all-expert activation and learned sparse-routing controls. These results show that task-structured routing provides an interpretable and compute-efficient decoder alternative for multi-task video-language learning.