[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-world-models":3},{"tag":4,"articles":11},{"id":5,"name":6,"slug":7,"article_count":8,"description_zh":9,"description_en":10},"7abda745-cf3a-41af-9493-634a33a600ce","world models","world-models",4,"世界模型指的是能從觀測中學習環境動態、再用來做預測與規劃的模型，常見於強化學習、機器人控制與影片理解。它之所以重要，在於能把 AI 從單次生成推向可推演、可決策的系統，並降低長期規劃與多代理互動的成本。","World models learn environment dynamics from observations so agents can predict outcomes and plan ahead. They matter in reinforcement learning, robotics, and video understanding, where latent planning, long-horizon control, and multi-agent action binding can reduce compute and improve decision quality.",[12],{"id":13,"slug":14,"title":15,"summary":16,"category":17,"image_url":18,"cover_image":18,"language":19,"created_at":20},"779f5798-9c39-4ce2-95d7-f0abfd24a695","five-ai-infra-frontiers-bessemer-2026-zh","Bessemer 看準的 5 個 AI 基礎設施前線","Bessemer 2026 AI infra 藍圖指向 memory、continual learning、RL、inference 與 world models。重點不是更大模型，而是讓 AI 真正進到生產環境。","industry","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775164388114-uo7t.png","zh","2026-04-02T21:12:39.852377+00:00"]