[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-chain-of-thought":3},{"tag":4,"articles":10,"peer_article_count":71},{"id":5,"name":6,"slug":6,"article_count":7,"description_zh":8,"description_en":9},"e61cb9bd-6313-4d74-81a1-4614874757e9","chain-of-thought",4,"Chain-of-thought 著重模型如何把多步推理串起來，而不只是給出最後答案。這個主題涵蓋長鏈推理、agent 迴圈、結構化輸出與長上下文下的穩定性，對評估與部署 LLM 很重要。","Chain-of-thought focuses on how models connect intermediate reasoning steps, not just final answers. It includes long-horizon benchmarks, agent loops, structured outputs, and stability under long context, all of which matter when evaluating and deploying LLMs.",[11,20,27,34,41,49,56,63],{"id":12,"slug":13,"title":14,"summary":15,"category":16,"image_url":17,"cover_image":17,"language":18,"created_at":19},"c649adb7-c8ae-4ade-a092-2c0d53beeb71","measuring-llm-behavior-portability-zh","LLM 行為不一定可移植","這篇研究指出，LLM 在一個情境學到的行為，常常無法穩定轉移到報酬等價但表面不同的環境。","research","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782717472977-na8g.png","zh","2026-06-29T07:17:29.597679+00:00",{"id":21,"slug":22,"title":23,"summary":24,"category":16,"image_url":25,"cover_image":25,"language":18,"created_at":26},"9f629b51-c1ad-4a83-beef-40059da1ab54","llms-stumble-counterintuitive-probability-zh","LLM 在反直覺機率題翻車","這篇研究發現，LLM 在標準機率題表現很高，但遇到反直覺、改寫或帶誤導提示的題目時，準確率會明顯下滑。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780900377752-3uk6.png","2026-06-08T06:32:28.84056+00:00",{"id":28,"slug":29,"title":30,"summary":31,"category":16,"image_url":32,"cover_image":32,"language":18,"created_at":33},"4a829d2a-24a3-42dd-8be4-49e5ab35435a","why-prompt-engineering-is-wrong-about-2026-zh","為什麼 2026 年 prompt engineering 錯了","2026 年真正決定 AI 輸出品質的不是 prompt 技巧，而是 context engineering；結構化輸入、範例與工具串接，才是降低錯誤與提升可重複性的關鍵。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780661884287-ow45.png","2026-06-05T12:17:19.813402+00:00",{"id":35,"slug":36,"title":37,"summary":38,"category":16,"image_url":39,"cover_image":39,"language":18,"created_at":40},"e3a4b0f7-03b3-43c6-ae51-906b337c5c2f","ipt-vlms-hidden-space-reasoning-zh","IPT 讓 VLM 更會想像隱藏空間","IPT 用中介感知 token 讓多模態模型學會推理看不到的空間結構，特別是在遮擋、視角切換與路徑追蹤上更準。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780468394735-1k40.png","2026-06-03T06:32:46.560029+00:00",{"id":42,"slug":43,"title":44,"summary":45,"category":46,"image_url":47,"cover_image":47,"language":18,"created_at":48},"b853a25a-5039-4591-9b0e-be17cc540ab7","prompt-engineering-vague-asks-usable-outputs-zh","Prompt engineering 讓模糊需求變可用輸出","我把 prompt engineering 拆成可直接抄的幾個寫法：怎麼寫約束、塞例子、控上下文，還有一份可貼進工作流的模板。","tools","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779340603269-p7eo.png","2026-05-21T05:15:54.226005+00:00",{"id":50,"slug":51,"title":52,"summary":53,"category":16,"image_url":54,"cover_image":54,"language":18,"created_at":55},"23a3d4c7-5cb7-40ae-a05b-1542364e786f","ibm-prompt-guide-turns-ai-guesses-into-outputs-zh","IBM 提示指南把猜答案變輸出","我把 IBM 的 prompt guide 拆成可直接上手的寫法，重點是怎麼把模糊提問改成可控輸出。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779132863293-etob.png","2026-05-18T19:33:55.711767+00:00",{"id":57,"slug":58,"title":59,"summary":60,"category":16,"image_url":61,"cover_image":61,"language":18,"created_at":62},"2468c20a-c3cf-4004-8981-44934691673a","longcot-long-horizon-chain-of-thought-benchmark-zh","LongCoT：測長鏈推理，不只看答案","LongCoT 用 2,500 題測試模型能否在長鏈、互相依賴的推理步驟中保持一致。GPT 5.2 與 Gemini 3 Pro 仍低於 10%。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1776319784084-uldi.png","2026-04-16T06:09:22.856744+00:00",{"id":64,"slug":65,"title":66,"summary":67,"category":68,"image_url":69,"cover_image":69,"language":18,"created_at":70},"f8c44ca5-e1b5-4b51-a7e5-61cdf8fa5ab9","prompt-engineering-agents-structured-outputs-zh","Agent 與結構化輸出提示詞實戰","LLM 進到生產環境後，提示詞不再是寫得漂亮就好。這篇拆解推理、長上下文、JSON 合約與 agent 迴圈，講清楚怎麼把 GPT、Claude 和本地模型用得更穩。","ai-agent","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775164928194-j63i.png","2026-04-02T21:21:45.59991+00:00",7]