前Deepmind研究员认为AlphaGo的游戏本可以治愈AI幻觉。
关键点
- 前Deepmind研究者 Thore Graepel建立了Metis Reasoning公司，目的是使AI系统更加可靠和可核查，并以AlphaGo的方法作为蓝图。
- 方法将知识与模型权重区分开来，并储存在外部分类账中，其中一个独立的模块对照证据检查每一项假设。
这种方法旨在减少幻觉，提供来源支持的解释，但迄今为止，它只是作为一个理论框架存在，没有工作系统。
前Deepmind研究者 Thore Graepel希望建立AI系统，追踪他们所知道的，并根据证据检查每个结论。 较大的语言模型不会解决这个问题，他的团队称。
Thore Graepel认为，让AlphaGo在2016年击败Go专业的李·塞多尔(英语：Lee Sedol)的同样想法可以使AI系统更加可靠，包括在机器人中。 前Deepmind研究员创办了Metis Reasoning公司，并在柏林的Nvidia的GTC会议上阐述了这一方法，芯片制造者一直在扩充其机器人和物理AI的平台，目的是将机器人的数据问题变成一个计算问题。
一个知识分类账，而不是知识烤成模型加权
今天的语言模型相当于只是AlphaGo的预测网络，根据Metis的说法，这暗示了可能的动作。 AlphaGo通过游戏序列的系统搜索将网络配对，并发现了著名的Move 37，这个动作这个网络可以给人类玩家大致带来十分之一的制作机会。 梅蒂斯想把这种搜索带给一般推理。
这样做需要公司纸上所说的明确的知识分类账，可以和游戏板相比。 类似的想法已经存在：Microsoft的GraphRAG在知识图中组织模型外的权重知识，Google的AlphaGeometry2将一个语言模型配对，并带有一个符号引擎来验证数学证明。 梅蒂斯希望将这些原则延伸至现实世界中的问题。
其知识分类账记录已知情况、系统的信心程度以及哪些问题仍未解答。 一个单独的模块对照计算，正式证明，或经验性证据来检查新条目，当一个假设消失时，每个依赖于它的结论都会得到重新评估。 "移动"可以是提问，实验，也可以是对一个现有假设的挑战；系统选择哪一个能减少最初问题的不确定性最多，最终给出答案或合理的弃权。
与AlphaGo一样，两个神经网络建议和评价各种动作，接受过事先记录调查的培训，但无法推翻核查规则。 Metis 称设置应产生较少的幻觉，用源代码解释，以及无需再培训即可更改或删除的知识。 由于推理突破小步，这种方法还可以有利于在边缘设备和机器人上运行的较小模型。
公司还不相信语言模型来部署它们
更多的数据和计算改进了语言模型，但不能解决技术的根本问题，据Metis说：模型编织答案，其推理链无法验证，其知识被锁在权重之内。 文件认为，幻觉来自培训过程本身，因为模型学会产生可信的文本而不是真实的文本，即使是今天的推理模型中的思想链也往往无法显示如何真正得到答案。
本文称是AI至今未产生多少经济影响的原因。 当agent代表公司行事时，错误很难追溯到源头，错误的知识不能从模型中去除。 AI在软件工程中起飞，因为代码容易测试和回滚，但在医学，金融，芯片设计，以及机器人方面，论文认为，AI需要从地面上进行验证。
白皮书只描述了一个研究计划，没有工作系统，没有基准测试，也没有与当前推理模型进行比较。 团队承认，与Go不同的是，现实世界只提供部分信息，证据可以昂贵获得，有些问题根本就没有答案。 最大的未决问题是，经过核实的知识分类账如何在教科书实例以外的混乱领域形成，因为象征性的人工智能系统一度失败，正是因为所有知识都必须手工输入。
在Silver和Rocktäschel之后，另一位Deepmind老兵独自出击
根据他的网站，格雷佩尔于2001年在柏林获得机器学习博士学位。 在Microsoft Research中，他帮助建立了TrueSkill，即Xbox Live背后的配对系统。 随后他搬到Deepmind，在那里他为AlphaGo,AlphaZero和MuZero工作。 在阿尔托斯实验室的一次施泰特后，他最近效力于Google Deepmind的AGI后球队，他在夏天离开。 他还在伦敦大学学院担任机器学习教席。 据彭博社报道，格雷佩尔为梅蒂斯筹集了几千万的低价轮，在作品中已经高估了几亿元的续作。
他是Deepmind在Google之外建造某种东西的最著名的突破的最新建筑师。 前任同事大卫·西尔弗(David Silver)创立了"无能情报"，并关闭了任何欧洲启动赛中最大的种子回合。 Tim Rocktäschel共同创办了Recursive，现价值46.5亿美元。 其他人会直接去找竞争对手据"金融时报"报道，Deepmind解散了在AlphaFold后面的球队，诺贝尔奖得主John Jumper与两名同事一同前往Anthropic. CNBC追踪到人才外流的部分原因是内部对Google的TPU芯片和官僚作风的获取有限，而Deepmind本身正在失去独立性，创始人Demis Hassabis已经退出日常业务。
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Former Deepmind researcher thinks AlphaGo's playbook can cure AI hallucinations
Key Points
- Former Deepmind researcher Thore Graepel founded Metis Reasoning to make AI systems more reliable and verifiable, using AlphaGo's approach as a blueprint.
- The method separates knowledge from model weights and stores it in an external ledger, where an independent module checks every assumption against evidence.
- The approach aims to cut hallucinations and provide source-backed explanations, but so far it exists only as a theoretical framework with no working system.
Former Deepmind researcher Thore Graepel wants to build AI systems that track what they know and check every conclusion against evidence. Larger language models won't fix this problem, his team says.
Thore Graepel believes the same idea that let AlphaGo beat Go professional Lee Sedol in 2016 can make AI systems more reliable, including in robots. The former Deepmind researcher founded Metis Reasoning and laid out the approach at Nvidia's GTC conference in Berlin, where the chipmaker has been expanding its platform for robotics and physical AI with the goal of turning robotics' data problem into a compute problem.
A knowledge ledger instead of knowledge baked into model weights
Today's language models amount to just AlphaGo's prediction network, which suggested possible moves, according to Metis. AlphaGo paired that network with a systematic search through game sequences and found the famous Move 37, a move the network would have given a human player roughly a one-in-ten-thousand chance of making. Metis wants to bring that kind of search to general reasoning.
Doing so requires what the company's paper calls an explicit knowledge ledger, comparable to the game board. Similar ideas already exist: Microsoft's GraphRAG organizes knowledge outside model weights in a knowledge graph, and Google's AlphaGeometry2 pairs a language model with a symbolic engine that verifies mathematical proofs. Metis wants to extend those principles to open questions in the real world.
Its knowledge ledger records what is known, how confident the system is, and which questions remain open. A separate module checks new entries against calculations, formal proofs, or empirical evidence, and when an assumption falls away, every conclusion that depended on it gets re-evaluated. A "move" can be a question, an experiment, or a challenge to an existing assumption; the system picks whichever one reduces uncertainty about the original question the most, ultimately delivering either an answer or a justified abstention.
As in AlphaGo, two neural networks suggest and evaluate moves, trained on logged prior investigations but unable to override the verification rules. Metis says the setup should produce fewer hallucinations, explanations with source citations, and knowledge that can be changed or deleted without retraining. Because reasoning breaks into small steps, the approach could also favor smaller models running on edge devices and in robots.
Companies still don't trust language models enough to deploy them
More data and compute improve language models but don't solve the technology's fundamental problems, according to Metis: the models fabricate answers, their reasoning chains can't be verified, and their knowledge is locked inside the weights. Hallucinations come from the training process itself, the paper argues, because models learn to produce plausible text rather than true text, and even the chains of thought in today's reasoning models often fail to show how an answer was actually reached.
That, the paper says, is why AI has had so little economic impact so far. When an agent acts on behalf of a company, errors are hard to trace back to their source and wrong knowledge can't be removed from the model. AI took off in software engineering because code is easy to test and roll back, but in medicine, finance, chip design, and robotics, the paper argues, AI needs to be verifiable from the ground up.
The white paper describes only a research program, with no working system, no benchmarks, and no comparisons with current reasoning models. The team acknowledges that the real world, unlike Go, provides only partial information, that evidence can be expensive to obtain, and that some questions simply have no answer. The biggest open question is how a verified knowledge ledger would take shape in messy domains beyond textbook examples, given that symbolic AI systems once failed precisely because all their knowledge had to be entered by hand.
After Silver and Rocktäschel, another Deepmind veteran strikes out on his own
Graepel earned his PhD in machine learning in Berlin in 2001, according to his website. At Microsoft Research he helped build TrueSkill, the matchmaking system behind Xbox Live. He then moved to Deepmind, where he worked on AlphaGo, AlphaZero, and MuZero. After a stint at Altos Labs he most recently served on Google Deepmind's post-AGI team, which he left over the summer. He also holds the machine learning chair at University College London. According to Bloomberg, Graepel is raising a low tens-of-millions round for Metis, with a follow-on of several hundred million dollars at a higher valuation already in the works.
He's the latest architect of Deepmind's best-known breakthroughs to build something outside Google. Former colleague David Silver founded Ineffable Intelligence and closed the largest seed round of any European startup. Tim Rocktäschel co-founded Recursive, now valued at $4.65 billion. Others are going straight to competitors. According to the Financial Times, Deepmind dissolved the team behind AlphaFold, and Nobel laureate John Jumper left for Anthropic with two colleagues. CNBC traces the talent drain partly to limited internal access to Google's TPU chips and bureaucracy, while Deepmind itself is losing its independence and founder Demis Hassabis has stepped back from day-to-day operations.
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