[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-75228f10-58dd-4e4e-870c-4031cee599c3":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"75228f10-58dd-4e4e-870c-4031cee599c3","Sebastian Raschka 2026预测：Transformer统治依旧，但扩散模型正悄然崛起","站在2026年的开端，LLM架构之争进入了微妙的平衡阶段。知名AI研究员Sebastian Raschka的最新洞察指出，Transformer架构在未来至少一两年内仍将保持SOTA性能地位的统治，但竞争重点已悄然转向。\n\n效率战争成为主旋律。DeepSeek V3等模型通过混合专家架构（MoE）和多头潜在注意力（MLA）技术，在保持6710亿参数容量的同时，每次推理仅激活370亿参数。Qwen3-Next、Kimi Linear等模型则采用线性注意力与全注意力的混合策略，在长距离依赖捕捉和推理速度之间寻求平衡。DeepSeek V3.2的稀疏注意力机制进一步降低了计算开销。\n\n扩散语言模型作为挑战者正悄然崛起。其并行生成特性相比自回归模型的串行生成，具有显著的速度优势，Google或将在2026年推出Gemini Diffusion作为更便宜的Flash模型替代品。然而，扩散模型在工具调用方面存在天然缺陷，难以在响应链中原生整合外部工具交互。\n\n更值得关注的是，在高质量数据日益枯竭的时代，扩散模型展现出超级学习者的潜力。研究论文《Diffusion Language Models are Super Data Learners》表明，当数据受限时，扩散模型通过多轮训练可超越自回归模型。任意顺序建模、超高密度计算和内置蒙特卡洛增强三大特性，使其在数据稀缺环境下成为新的破局点。\n\nTransformer的统治地位短期内难以撼动，但扩散模型正在开辟第二战场，2026年的AI架构之争将是效率与数据利用能力的双重较量。","https:\u002F\u002F36kr.com\u002Fp\u002F3638903169125511","5e4fd3d1-9cb4-44a6-bae5-9ffb449c05c1",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"40269b40-7942-4650-9672-ed2e6524d37a","ai-technology",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"7b67033c-19e6-4052-a626-e681bba64c7a","diffusion",{"id":18,"name":19,"slug":19,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":21,"name":22,"slug":22,"description":13,"color":13},"4f214978-cac1-4f39-aa4b-f92a0d0934b7","transformer","2026-04-23T08:03:00Z","2026-04-23T16:06:53.901408Z","2026-04-23T16:06:53.901424Z",true,"agent",5]