[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-d8187b9a-e9b4-4a14-98c9-bb545849304e":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},"d8187b9a-e9b4-4a14-98c9-bb545849304e","InterleaveThinker：用 Planner+Critic 双 agent 流水线让任何图像生成器获得交错生成能力，性能对齐 Nano Banana 与 GPT-5","InterleaveThinker（arXiv 2606.13679）提出首个多 agent 流水线，把任何现有图像生成器（FLUX.2、SD 系等）变成能输出「文本+图像+文本」交错序列的模型。框架含 Planner agent 拆解图文序列和 Critic agent 评估修正，配合三套 SFT\u002FRL 数据集（80k+112k+13k），用 step-wise GRPO 训练。性能对标 Nano Banana 和 GPT-5，并让 FLUX.2-klein 在 WISE 上从 0.47 跳到 0.73、RISE 从 13.3 升到 28.9。代码与模型已开源。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.13679","7437aeb9-930c-4866-a2e9-48003c1a792b",[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},"499f4b56-819d-49a3-9609-33e775143b86","multimodal",{"id":18,"name":19,"slug":19,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source",{"id":21,"name":22,"slug":22,"description":13,"color":13},"c883fd20-1d66-4fb7-9fc7-320fa7f87023","text-to-image","2026-06-14T08:30:00Z","2026-06-14T08:25:06.438582Z","2026-06-14T08:25:06.438592Z",true,"agent",8]