[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-604c331d-9627-4f95-a721-80e83d0b5ba7":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},"604c331d-9627-4f95-a721-80e83d0b5ba7","Meta 发布 Muse Spark：原生多模态推理模型革新视觉理解","Meta 人工智能团队发布 Muse 系列首款模型 **Muse Spark**，一款原生多模态推理模型，在视觉理解和医学推理任务上实现显著突破，展现出相较于主流竞品的明显性能优势。\n\n与常见「视觉模块嫁接语言模型」的方案不同，Muse Spark 从训练阶段起即原生整合文本与视觉信息，实现真正的跨模态联合推理。在 UI 元素定位基准 ScreenSpot Pro 上，Muse Spark 得分 72.2（使用 Python 工具可达 84.1），远超 GPT-5.4 Xhigh 的 39.0，也明显领先 Claude Opus 4.6 Max 的 57.7。\n\n医学推理是另一大亮点。在 HealthBench Hard 评估中，Meta 通过与超过 1000 名临床医生合作构建的专业医学数据集进行微调，Muse Spark 成绩超越 GPT-5.4 超过 2 个百分点。CharXiv Reasoning 科学图表分析基准上同样领先。\n\n更值得关注的是效率提升：Meta 透露，Muse Spark 达成与 Llama 4 Maverick 同等能力所需算力减少了**一个数量级以上**，这得益于预训练架构、优化流程和数据管理的系统性改进。Muse Spark 将于未来数周内逐步集成至 Meta AI 消费端服务，API 已进入私有预览。","https:\u002F\u002Fai.meta.com\u002Fblog\u002Fintroducing-muse-spark-msl\u002F","a1f0bda7-5035-4317-b63b-72693539d2e3",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"5e628969-6d2a-437f-998a-104e4b16cfb1","ai-progress",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},"499f4b56-819d-49a3-9609-33e775143b86","multimodal",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b1853a5a-d940-42b7-94f9-0488ee3f2cf7","new-model","2026-04-26T16:05:00Z","2026-04-26T16:06:49.655067Z","2026-04-26T16:06:49.655083Z",true,"agent",3]