[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-bifocal-dllm-r2lm-mamba-qwen3-1-7b":3,"news-related-bd1a9589-0cca-4f68-a90d-3e454e94554f":36},{"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},"bd1a9589-0cca-4f68-a90d-3e454e94554f","Bifocal dLLM：Mamba 旁路解 KV 困局，吞吐 2.4×–12.9×","扩散语言模型 dLLM 一直被「KV 缓存 + 双向上下文」二选一困住：双向注意力质量好但吃不上 KV 缓存，纯因果能用缓存但丢光右侧上下文。arXiv 2606.27732 提出 Bifocal dLLM 新范式并实例化为 R2LM（Right-to-Left Mamba），主干沿用带 KV 缓存兼容的标准因果注意力负责左侧精确上下文，旁路挂一条轻量反向 Mamba SSM 压缩表达右侧上下文，左右「双焦」拼接出双向信息。论文在 Qwen3-1.7B 上做 60B token 继续预训练，结果 R2LM 相比双向 dLLM 跑出 2.4×–12.9× 吞吐，批服务下相对 AR 基线 1.9×–2.9× 提速，质量在多数基准上超过纯因果基线、平均分压过双向 dLLM。解码主路径仍是带缓存的注意力，旁路只是 SSM 状态，能与现有 vLLM、KV 压缩方案直接叠加。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.27732","7437aeb9-930c-4866-a2e9-48003c1a792b",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"7b67033c-19e6-4052-a626-e681bba64c7a","diffusion",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",[24],{"id":25,"lang":26,"title":27,"summary":28,"content":13},"a6bae56c-3fa7-4b67-96b4-c87579870cee","en","Bifocal dLLM: Mamba bypass fixes KV strain, 2.4-12.9x throughput","Diffusion language models (dLLMs) have long been trapped by a binary choice between \"KV cache\" and \"bidirectional context\": bidirectional attention is high quality but cannot use KV cache; pure causal can use cache but loses all right-side context. arXiv 2606.27732 introduces the Bifocal dLLM paradigm and instantiates it as R2LM (Right-to-Left Mamba): the main path keeps standard causal attention compatible with KV caching to handle exact left-side context, while a lightweight reverse Mamba SSM on a side-path compresses the right-side context, and the two \"focal\" sides are concatenated for bidirectional information.\n\nThe paper continues pretraining on Qwen3-1.7B with 60B tokens: R2LM achieves 2.4×–12.9× throughput over bidirectional dLLMs, 1.9×–2.9× speedup over AR baselines in batched serving, and surpasses pure causal baselines on most benchmarks while beating bidirectional dLLM on the average. The main decoding path still uses cacheable attention; the side-path is just an SSM state, so it stacks directly with existing vLLM and KV-compression solutions.","bifocal-dllm-r2lm-mamba-qwen3-1-7b","2026-06-29T10:08:00Z","2026-06-29T02:11:41.752731Z","2026-08-19T02:08:40.142862Z",true,"agent",105,{"items":37},[38,43,48,53,58,63],{"id":39,"title":40,"news_slug":41,"published_at":42},"3dab673e-0bdc-442a-9670-87964ebf8f79","Dynamic-dLLM：动态缓存预算+自适应并行解码，给扩散语言模型提速 3 倍","dynamic-dllm-cache-budget-3x","2026-06-25T10:00:00+00:00",{"id":44,"title":45,"news_slug":46,"published_at":47},"0b744f65-a3d5-47d8-84d1-eb25a7a2798e","FMLM+ 把扩散语言模型的「自纠错」解锁：32× 更少 NFE 匹配离散基线","fmlm-plus-posterior-refinement-32x-nfe","2026-06-25T02:30:00+00:00",{"id":49,"title":50,"news_slug":51,"published_at":52},"80bc5e25-d24d-45a1-9c2b-534ebcae39f9","腾讯 WeDLM 开源：让扩散 LLM 在标准因果注意力下跑出 3-6× vLLM 加速","tencent-wedlm-diffusion-llm-causal-3-6x","2026-06-16T20:00:00+00:00",{"id":54,"title":55,"news_slug":56,"published_at":57},"634ba8f7-771e-4492-aacb-549112a8c91a","EPIC 让扩散语言模型重获并行优势：CFG 约束解码推理时间压缩 67.5%","epic-cfg-dllm-67-5pct-speedup","2026-06-09T16:00:00+00:00",{"id":59,"title":60,"news_slug":61,"published_at":62},"b45f5c46-c982-4410-9452-07a9f779218f","On-Policy Distillation 把扩散语言模型训练成本砍到 1\u002F15~1\u002F7000","opdlm-on-policy-distillation-dllm-1-7000x","2026-06-08T04:00:00+00:00",{"id":64,"title":65,"news_slug":66,"published_at":67},"aa9841f2-87cd-48dc-a3cf-e0e47367b0af","UltraFlux：CVPR 2026稀疏注意力优化方案，4K上下文重建质量与效率双突破","ultraflux-cvpr-2026-4k-resonance-rope","2026-06-03T22:02:00+00:00"]