[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-sebastian-raschka-2026-transformer-vs-diffusion-moe":3,"topics-all":36,"news-related-75228f10-58dd-4e4e-870c-4031cee599c3":55},{"id":4,"title":5,"summary":6,"content":6,"original_url":7,"source_id":8,"tags":9,"translations":23,"news_slug":29,"published_at":30,"created_at":31,"modified_at":32,"is_published":33,"publish_type":34,"image_url":13,"view_count":35},"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",[24],{"id":25,"lang":26,"title":27,"summary":28,"content":13},"5bd6ad80-0a27-490b-8efa-81dff7aab04a","en","Raschka's 2026 forecast: Transformers rule, diffusion rising","Standing at the start of 2026, the LLM architecture debate has reached a delicate equilibrium. Renowned AI researcher Sebastian Raschka's latest insight: Transformer architectures will continue to hold the SOTA performance crown for at least the next one to two years — but the competitive focus has quietly shifted.\n\nThe efficiency war is the new main theme. Models like DeepSeek V3 use Mixture of Experts (MoE) and Multi-head Latent Attention (MLA) to keep a 671B-parameter capacity while activating only 37B per inference. Qwen3-Next and Kimi Linear adopt hybrid strategies that mix linear and full attention, balancing long-range dependency capture with inference speed. DeepSeek V3.2's sparse attention mechanism further cuts compute overhead.\n\nDiffusion language models are quietly emerging as challengers. Their parallel-generation nature offers a significant speed advantage over the serial generation of autoregressive models, and Google may release Gemini Diffusion in 2026 as a cheaper Flash-model alternative. Yet diffusion models have a natural defect in tool calling — it is hard to natively integrate external tool interactions into the response chain.\n\nMore notably, in an era of increasingly scarce high-quality data, diffusion models show \"super learner\" potential. The paper \"Diffusion Language Models are Super Data Learners\" shows that under data-constrained conditions, diffusion models can surpass autoregressive models through multi-round training. Three properties — arbitrary-order modeling, ultra-high-density compute, and built-in Monte Carlo augmentation — make them a new breakthrough path when data is scarce.\n\nTransformer's dominance won't be easily shaken in the short term, but diffusion models are opening a second front. The 2026 AI architecture race will be a dual contest of efficiency and data-utilization capability.","sebastian-raschka-2026-transformer-vs-diffusion-moe","2026-04-23T08:03:00Z","2026-04-23T16:06:53.901408Z","2026-08-19T02:08:40.142862Z",true,"agent",235,[37,46],{"slug":38,"tag_slug":38,"title_zh":39,"title_en":40,"intro_zh":41,"intro_en":42,"id":43,"is_active":33,"created_at":44,"modified_at":45},"ai-for-science","AI for Science 2026：从 UniPert 到 GPT-Rosalind 的硬核进化","AI for Science 2026: from UniPert to GPT-Rosalind","生命科学、化学材料、物理世界模型——AI 正在从\"语言工具\"变成\"实验伙伴\"。本专题收录 AI 在三大科学方向的关键节点：UniPert 统一基因与化学扰动空间、GPT-Rosalind 端到端生命科学推理、达摩院 AI 智能体 28 小时找到 4 种超导新材料、Anthropic Claude Science 把工作台做成标准品。","From language tool to lab partner — AI is reshaping life sciences, chemistry\u002Fmaterials, and physical world models. This topic covers the key milestones: UniPert unifying genetic-chemical perturbation spaces, GPT-Rosalind's end-to-end life-sciences reasoning, DAMO's AI agent discovering 4 superconducting materials in 28 hours, and Anthropic's Claude Science workbench going mainstream.","988a4300-5fab-41c4-b5d8-63711a2dc757","2026-09-10T01:34:15.296649Z","2026-09-10T01:34:15.296663Z",{"slug":47,"tag_slug":47,"title_zh":48,"title_en":49,"intro_zh":50,"intro_en":51,"id":52,"is_active":33,"created_at":53,"modified_at":54},"h3-series","MiniMax H3 系列：从开源权重到 35 倍吞吐","MiniMax H3 Series: from open weights to 35x throughput","MiniMax H3 自 2026 年 8 月开源以来节奏密集：官方把生成、参考与编辑收回一个模型；ComfyUI 当天压进 RTX 3060；摩尔线程 3 小时完成国产 GPU 适配；fal 后训练版把吞吐拉到 35 倍；FastH3 蒸馏再砍推理成本。本专题持续追踪 H3 的发布—开源—蒸馏—部署全链路。","Since MiniMax open-sourced H3 in August 2026 the pace has been relentless: one unified omni-modal model, same-day ComfyUI support down to an RTX 3060, a 3-hour Day-0 port to Moore Threads GPUs, fal's post-trained H3 Max at 35x throughput, and FastH3 distillation cutting inference cost further. This topic tracks the full H3 chain — release, open weights, distillation, deployment.","83ef0daa-3c31-4cb3-86ed-e5ee58654d5f","2026-09-08T07:33:19.942193Z","2026-09-08T07:33:19.942209Z",{"items":56},[57,62,67,72,77,82],{"id":58,"title":59,"news_slug":60,"published_at":61},"0a3f5044-ed20-4c2a-b710-bd26cd276d3e","ALiBi 的隐藏数值故障：长上下文越长，部分注意力头越可能“失明”","alibi-attention-underflow-long-context","2026-08-06T10:30:00+00:00",{"id":63,"title":64,"news_slug":65,"published_at":66},"e3f049f5-2f0d-48d2-8e88-246ef006fa16","LoopMTP 给循环 Transformer 装上前瞻路标：固定参数下让每一轮都做不同的事","loopmtp-latent-multi-token-loop-guidance","2026-08-04T13:13:09+00:00",{"id":68,"title":69,"news_slug":70,"published_at":71},"217f417d-1b9c-475b-99f4-e21e7c909711","MHAR 把 Transformer 残差流从「单车道」拆成 H 条独立路由:子空间第一次有权自己挑历史层","multi-head-attention-residuals-mhar","2026-08-01T07:30:00+00:00",{"id":73,"title":74,"news_slug":75,"published_at":76},"cb7fb8b3-5862-4cba-adab-c4794e989966","图灵奖得主 Pearl 长访谈：LLM 能讲因果只是因为人类替它爬过了因果阶梯","judah-pearl-llm-causal-ladder-agi","2026-07-31T07:00:00+00:00",{"id":78,"title":79,"news_slug":80,"published_at":81},"f8436dd3-d6fc-4ea7-9f2e-1086026c11d0","Transformer 的几何之眼：arXiv 2607.17146 把注意力炼成薛定谔桥，把 SGD 写成伊藤扩散","transformer-geometry-schrodinger-bridge","2026-07-23T12:10:00+00:00",{"id":83,"title":84,"news_slug":85,"published_at":86},"e54f030e-14ed-4262-9dd9-8685fdbb03ab","DiscoLoop 把循环 Transformer 的「表征瓶颈」焊死:双通道架构让多跳推理一步到位","discoloop-dual-channel-recurrent","2026-07-20T08:00:00+00:00"]