[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-ai-10min-erodes-persistence-berkeley":3,"topics-all":35,"news-related-f6070383-1b76-41d2-9a63-618542214a79":54},{"id":4,"title":5,"summary":6,"content":7,"original_url":8,"source_id":9,"tags":10,"translations":21,"news_slug":28,"published_at":29,"created_at":30,"modified_at":31,"is_published":32,"publish_type":33,"image_url":14,"view_count":34},"f6070383-1b76-41d2-9a63-618542214a79","用AI十分钟就废专注力:伯克利CoLM实验","UC伯克利联合CMU、MIT、Oxford与UCLA的研究显示,只用ChatGPT辅助解题10分钟,被撤掉AI工具后用户解题准确率立刻断崖式下跌、跳过率飙升;三组随机对照实验共1222人参与,论文已在CoLM会议同行评审发表。","刷 15 道基础分数题,前 11 道有 ChatGPT 帮你、它在第 12 题被悄悄拿走——这是 UC 伯克利最新论文里最受关注的实验。10 月 9 日,UC 伯克利联合卡内基梅隆、MIT、牛津、UCLA 的研究团队,把经过同行评审的版本发表在 CoLM 会议上。\n\n研究设计相当克制:第一项实验招募 354 名参与者,一组独立解题,另一组全程开着 ChatGPT、可以问、可以直接抄答案。AI 组前 11 道题的正确率稳定高于对照组——但 ChatGPT 一旦被撤掉,参与者几乎全线停摆;从未用过 AI 的那组则坚持做完、稳中有升。第二项实验放大到 667 人,结果几乎一致;第三项 201 人,材料换成 SAT 阅读理解,模式再次复现。三轮实验 1222 名参与者。\n\n## 这不能简单归类为「懒惰」\n\n作者用了一个更准确的词:\"productive struggle\"——有生产力的挣扎。他们认为解题过程中那些让你皱眉、想换思路、推倒重来的时刻,才是大脑形成长期能力的关键时段。AI 把这种挣扎直接抽走,短期看节省时间,但撤掉 AI 之后,使用者既失去「我已掌握」的元认知反馈,也没在挣扎里学到任何东西。这就解释了「AI 组一旦失去工具,既会答错也会放弃」——他们从一开始就没完成那段挣扎。\n\n通讯作者、《The Alignment Problem》作者 Brian Christian 直接点出让 AI 行业不太好过的判断:\"我们造了这些系统让它们有用,但讽刺的是,我们证明它们常常是以一种不太有用的方式在帮我们。\"Anthropic 自己年初也承认,把任务交给聊天机器人会降低对基础知识的掌握度。\n\n## 对 AI 产品形态的一记伏击\n\n这记刀不是砸向学生,而是 AI 产品默认形态的设计者。当下几乎所有大模型产品,默认行为都是「快速给出最终答案」——恰好是 Christian 主张要反对的形态。他给出的替代方案很明确:AI 应默认像家教那样一步步引导、提问、留白,把 productive struggle 还给使用者。如果落实,意味着所有\"先给结论\"的 Agent 工作流要重新排序,长思考模式不再是\"更努力\"的炫耀,而是产品合格线。\n\n更现实的还有研究者自己。Christian 担忧科学界对 AI 的过度依赖会伤害\"好科学家\"的本质:贴近数据、沉浸材料、团队协作。当原本 10 人的研究小组被压缩成 2、3 人加一个聊天机器人,「一些重要的东西正在被丢掉」。\n\n## 所以呢\n\n方法并不花哨——三组随机对照、一种范式、一个容易复制的实验设计。它能上同行评审并引发《纽约时报》《WIRED》《Psychology Today》跟进,说明「AI 把人变懒」已从民间怀疑升级成有学术共识的现象级焦虑。在监管开始盯「AI 对劳动力能力的腐蚀」、欧盟 AI Act 也在补这一环的背景下,2027 年的 AI 产品很可能要正面回答:你的默认回答是「答案」还是「教练」?\n\n论文 arxiv: 2604.04721,题为《AI Assistance Reduces Persistence and Hurts Independent Performance》。","https:\u002F\u002Fnews.berkeley.edu\u002F2026\u002F10\u002F09\u002Fusing-ai-for-just-10-minutes-erodes-your-ability-to-persist-at-hard-things\u002F","c44d7c85-303f-4faa-a9e7-7ed3f3b4e972",[11,15,18],{"id":12,"name":13,"slug":13,"description":14,"color":14},"5e628969-6d2a-437f-998a-104e4b16cfb1","ai-progress",null,{"id":16,"name":17,"slug":17,"description":14,"color":14},"1fcfaaf2-67de-43d3-9e35-5784852fec60","ai-safety",{"id":19,"name":20,"slug":20,"description":14,"color":14},"01598627-1ea6-4b27-a5d8-874971571a71","llm",[22],{"id":23,"lang":24,"title":25,"summary":26,"content":27},"efe2a691-4549-4972-b532-67bb6c932c59","en","AI for 10 minutes erodes persistence, Berkeley study finds","UC Berkeley + 4 universities: 10 min of ChatGPT erodes persistence once removed. 1,222 participants, 3 randomized trials, peer-reviewed at CoLM.","Solve 15 basic fraction problems with ChatGPT helping you through the first 11 — then quietly take the assistant away at question 12. That setup drove the most-discussed experiment in a new UC Berkeley paper. On October 9, a multi-institution team from UC Berkeley, Carnegie Mellon, MIT, Oxford and UCLA presented the peer-reviewed version of the study at the Conference on Language Modeling (CoLM).\n\nThe experimental design is unglamorous and therefore credible. In the first trial, 354 participants were recruited online: one group solved the problems unaided, the other had ChatGPT open on the side and could even paste the answer. The AI group's accuracy stayed comfortably above the control group's through the first 11 problems — but the moment the AI was withdrawn, that orange line collapsed to the floor. The unaided group kept going, and even improved slightly by question 15. A second trial with 667 participants repeated the pattern almost exactly; a third trial with 201 participants swapped the math problems for SAT reading comprehension and got the same answer. 1,222 randomized participants in total.\n\n## This is not simply \"laziness\"\n\nThe authors prefer a sharper word: \"productive struggle.\" They argue that the moments in problem-solving where you frown, change tack, and scrap a flawed approach are exactly what allow the brain to consolidate long-term competence. AI removes that struggle outright. In the short run it looks like saved time — but once the tool is taken away, users have neither the metacognitive feedback of \"I have actually understood this\" nor any of the new skill that struggle would have produced. That is why \"the AI group, once deprived of the tool, got things wrong and gave up\" — they never finished the struggle in the first place.\n\nThe paper's senior author, Brian Christian (author of The Alignment Problem), puts the result in a way the AI industry will not enjoy hearing: \"We've built these systems to be helpful, but are they being helpful to us in a way that's actually helping us? Ironic — we showed they often help us in ways that are kind of unhelpful.\" Anthropic itself has already acknowledged, in a separate report this year, that offloading tasks to chatbots erodes baseline understanding.\n\n## A quiet rebuke of current product defaults\n\nThe blade here is not aimed at students. It is aimed at the designers of the current default behaviour of every major AI product. Today's frontier assistants are tuned to deliver \"the final answer, fast\" — which is exactly the behaviour Christian argues against. His proposed alternative is direct: AI should default to behaving like a tutor — guiding, questioning, leaving space — so that productive struggle is returned to the user. Read literally, that would force every \"answer-first\" agentic workflow to be re-sequenced, and turn long-thinking mode from a marketing flex into a baseline quality bar.\n\nThere is also a more uncomfortable audience: researchers themselves. Christian worries that over-reliance on AI tools in science jeopardises what makes a good scientist — staying close to data, being immersed in the material, working as part of a team. When a research group of ten shrinks to two or three people and a chatbot, \"something significant is being lost.\"\n\n## So what\n\nThe method is unflashy: three randomized controlled trials, one paradigm, an experiment easy enough to replicate. The fact that this passed peer review at CoLM and was picked up by The New York Times, WIRED and Psychology Today means \"AI is making people lazy\" has graduated from folk suspicion to a published academic concern. With regulators starting to focus on \"AI's erosion of human capability\" — and the EU AI Act already edging in that direction — 2027 AI products will probably have to answer one small product question in public: is your default response an answer, or a coach?\n\nPaper: arXiv 2604.04721, \"AI Assistance Reduces Persistence and Hurts Independent Performance.\"","ai-10min-erodes-persistence-berkeley","2026-10-11T09:00:00Z","2026-10-11T09:05:12.795951Z","2026-10-11T09:05:12.795959Z",true,"agent",132,[36,45],{"slug":37,"tag_slug":37,"title_zh":38,"title_en":39,"intro_zh":40,"intro_en":41,"id":42,"is_active":32,"created_at":43,"modified_at":44},"ai-for-science","AI for Science 2026：从 UniPert 到 GPT-Rosalind 的硬核进化","AI for Science 2026: from UniPert to GPT-Rosalind","生命科学、化学材料、物理世界模型——AI 正在从\"语言工具\"变成\"实验伙伴\"。本专题收录 AI 在三大科学方向的关键节点：UniPert 统一基因与化学扰动空间、GPT-Rosalind 端到端生命科学推理、达摩院 AI 智能体 28 小时找到 4 种超导新材料、Anthropic Claude Science 把工作台做成标准品。","From language tool to lab partner — AI is reshaping life sciences, chemistry\u002Fmaterials, and physical world models. This topic covers the key milestones: UniPert unifying genetic-chemical perturbation spaces, GPT-Rosalind's end-to-end life-sciences reasoning, DAMO's AI agent discovering 4 superconducting materials in 28 hours, and Anthropic's Claude Science workbench going mainstream.","988a4300-5fab-41c4-b5d8-63711a2dc757","2026-09-10T01:34:15.296649Z","2026-09-10T01:34:15.296663Z",{"slug":46,"tag_slug":46,"title_zh":47,"title_en":48,"intro_zh":49,"intro_en":50,"id":51,"is_active":32,"created_at":52,"modified_at":53},"h3-series","MiniMax H3 系列：从开源权重到 35 倍吞吐","MiniMax H3 Series: from open weights to 35x throughput","MiniMax H3 自 2026 年 8 月开源以来节奏密集：官方把生成、参考与编辑收回一个模型；ComfyUI 当天压进 RTX 3060；摩尔线程 3 小时完成国产 GPU 适配；fal 后训练版把吞吐拉到 35 倍；FastH3 蒸馏再砍推理成本。本专题持续追踪 H3 的发布—开源—蒸馏—部署全链路。","Since MiniMax open-sourced H3 in August 2026 the pace has been relentless: one unified omni-modal model, same-day ComfyUI support down to an RTX 3060, a 3-hour Day-0 port to Moore Threads GPUs, fal's post-trained H3 Max at 35x throughput, and FastH3 distillation cutting inference cost further. This topic tracks the full H3 chain — release, open weights, distillation, deployment.","83ef0daa-3c31-4cb3-86ed-e5ee58654d5f","2026-09-08T07:33:19.942193Z","2026-09-08T07:33:19.942209Z",{"items":55},[56,61,66,71,76,81],{"id":57,"title":58,"news_slug":59,"published_at":60},"cdcd6b11-8a65-4b15-9add-6fa9343de5a3","十分钟 AI 协助后,人为何突然不愿再解题","arxiv-2604-04721-ai-persistence-reduces","2026-10-17T07:00:00+00:00",{"id":62,"title":63,"news_slug":64,"published_at":65},"0141e95e-e10e-4d88-bf4a-4d1e7df9bfaa","十分钟用 AI 就让人失去攻克难题的意志力","ten-minutes-ai-erodes-persistence-hard-tasks","2026-10-16T03:00:00+00:00",{"id":67,"title":68,"news_slug":69,"published_at":70},"fd82590e-7b61-4027-a6a7-8a2e3200bee5","用AI十分钟就削弱攻坚意志,CoLM研究实锤","ai-erodes-persistence-colm-2026","2026-10-11T00:00:00+00:00",{"id":72,"title":73,"news_slug":74,"published_at":75},"aa6a2681-ae9f-49b6-b8a4-fcca25f0f334","答对≠想对:亚利桑那州立实锤CoT思维链31.6%无效","cot-traces-invalid-arizona-state","2026-09-30T02:00:00+00:00",{"id":77,"title":78,"news_slug":79,"published_at":80},"144fa9dc-de03-4972-a695-3d392f334772","PubMed 中央库研究:2025 年生物医学论文 77% 有 LLM 写作痕迹","pubmed-77-percent-llm-writing-2025","2026-08-26T01:00:00+00:00",{"id":82,"title":83,"news_slug":84,"published_at":85},"cb7fb8b3-5862-4cba-adab-c4794e989966","图灵奖得主 Pearl 长访谈：LLM 能讲因果只是因为人类替它爬过了因果阶梯","judah-pearl-llm-causal-ladder-agi","2026-07-31T07:00:00+00:00"]