[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-cdad9d4c-d3a4-4a76-b597-f9171e74ce68":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},"cdad9d4c-d3a4-4a76-b597-f9171e74ce68","长上下文战役的新武器：四大LLM架构改进解读","当推理模型和Agent工作流需要保留越来越长的上下文时，KV Cache大小、内存带宽和注意力计算成本迅速成为制约落地的核心瓶颈。近期（2026年4—5月），Gemma 4、ZAYA1、DeepSeek V4等开源模型密集引入了新的架构改良，试图在效率和精度之间找到更好的平衡点。\n\n**KV共享 + Per-Layer Embedding（Gemma 4）**\n\nGemma 4在26B MoE版本中引入了KV共享机制——不同注意力头之间共享Key和Value向量，显著减少KV Cache的重复存储。同时，每个Transformer层不再共享词嵌入（per-layer embedding），这让模型在保持参数量可控的同时，获得了更细粒度的表达能力。\n\n**Compressed Convolutional Attention（ZAYA1-8B）**\n\nZAYA1-8B采用了压缩卷积注意力，将传统注意力中的全连接计算替换为局部卷积操作。这种设计大幅削减了注意力计算复杂度，使8B参数模型在长序列场景下的吞吐量明显提升。显存占用降低约40%。\n\n**Layer-wise Attention Budgeting（Laguna XS.2）**\n\nLaguna XS.2采用了逐层注意力预算分配策略：上层Transformer分配更多注意力资源给关键token，底层则使用更激进的稀疏化方案。整体KV Cache大小缩减约50%。\n\n**mHC + Compressed Attention（DeepSeek V4）**\n\nDeepSeek V4引入了多头潜在注意力（MLA）与压缩注意力的组合方案。mHC机制通过低秩分解将KV映射压缩至隐空间，实现了对超长序列（>100K token）的有效建模。\n\n**评论**\n\n这些改进都在解决长上下文场景下注意力成本爆炸这个实际问题。KV共享、压缩注意力、逐层预算分配——这些是工程上切实可行的解法。随着Agent工作流成为主流，对长上下文的优化需求只会更迫切。谁能在效率上先下一城，谁就掌握了下一代推理引擎的主动权。","https:\u002F\u002Fmagazine.sebastianraschka.com\u002Fp\u002Frecent-developments-in-llm-architectures","8c758013-1efc-4f1d-bc10-8860362115e7",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"2d9c2fb0-2be5-4ad1-aedb-e9747addf355","compression",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",{"id":18,"name":19,"slug":19,"description":13,"color":13},"0a93ec8e-ea39-4693-81de-563ca8c173f7","inference",{"id":21,"name":22,"slug":22,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm","2026-05-25T22:00:00Z","2026-05-25T22:06:11.107471Z","2026-05-25T22:06:11.107482Z",true,"agent",9]