[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-chain-of-thought":3},{"tag":4,"articles":10,"peer_article_count":64},{"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],{"id":12,"slug":13,"title":14,"summary":15,"category":16,"image_url":17,"cover_image":17,"language":18,"created_at":19},"c89012a2-8d2a-4abc-8325-2a6249828718","llms-stumble-counterintuitive-probability-en","LLMs stumble on counterintuitive probability","A benchmark finds LLMs are strong on standard probability problems but falter on counterintuitive ones.","research","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780900377596-25f1.png","en","2026-06-08T06:32:29.37299+00:00",{"id":21,"slug":22,"title":23,"summary":24,"category":16,"image_url":25,"cover_image":25,"language":18,"created_at":26},"aadc9843-d668-4507-8c2b-5eea7f352bb6","why-prompt-engineering-is-wrong-about-2026-en","Why Prompt Engineering Is Wrong About 2026","Prompt engineering is giving way to context engineering, and structured frameworks win because they reduce errors and improve repeatability.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780661867824-506z.png","2026-06-05T12:17:20.457075+00:00",{"id":28,"slug":29,"title":30,"summary":31,"category":16,"image_url":32,"cover_image":32,"language":18,"created_at":33},"a65ad2e8-de08-4108-82cb-c3737a17ac6f","ipt-vlms-hidden-space-reasoning-en","IPT helps VLMs reason about hidden space","Imaginative Perception Tokens improve multimodal models’ ability to reason about unseen spatial structure.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780468449119-aqbt.png","2026-06-03T06:32:47.048757+00:00",{"id":35,"slug":36,"title":37,"summary":38,"category":16,"image_url":39,"cover_image":39,"language":18,"created_at":40},"4e2d39c9-e078-498b-90ca-988afae7b79f","what-large-language-models-are-how-they-work-en","What large language models are, and how they work","Large language models turn huge text corpora into systems that generate, summarize, and reason with language.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779341169797-ssad.png","2026-05-21T05:25:43.849628+00:00",{"id":42,"slug":43,"title":44,"summary":45,"category":46,"image_url":47,"cover_image":47,"language":18,"created_at":48},"d6c6a56e-ccd8-4f41-a702-c56017cb5031","prompt-engineering-vague-asks-usable-outputs-en","Prompt engineering turns vague asks into usable outputs","I break down prompt engineering into practical patterns, with a copy-ready template for better LLM outputs.","tools","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779340581936-1nwz.png","2026-05-21T05:15:54.824671+00:00",{"id":50,"slug":51,"title":52,"summary":53,"category":16,"image_url":54,"cover_image":54,"language":18,"created_at":55},"9f62add5-cae5-47eb-abd5-2e56d0d5698c","longcot-long-horizon-chain-of-thought-benchmark-en","LongCoT Benchmark: 2,500-Probl. Long-Horizon Reasoning","LongCoT is a 2,500-problem benchmark for measuring whether frontier models can sustain long, interdependent reasoning chains.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1776319782523-s0wz.png","2026-04-16T06:09:23.265233+00:00",{"id":57,"slug":58,"title":59,"summary":60,"category":61,"image_url":62,"cover_image":62,"language":18,"created_at":63},"28a1b97c-06c1-4112-8fb5-a9ff8e58fcd9","prompt-engineering-agents-structured-outputs-en","Prompt Engineering for Agents and Structured Outputs","Prompt engineering gets harder in production: reasoning, long contexts, JSON contracts, and agent loops all need different prompt tactics.","ai-agent","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775164941484-fp41.png","2026-04-02T21:21:45.840568+00:00",8]