@roydanroy
@Google DeepMind. On leave, Canada CIFAR AI Chair and Former Research Director, @VectorInst. Professor, @UofT (Statistics/CS). Views are my own.
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The person who left JMLR argues top AI models now give better reviews than time-pressed humans.
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A fixed output shift helped later in training, but @mufan_li disputes the authors’ explanation.
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The authors say previous results reached either the optimal approximation scale or sample complexity, but not both.
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A user linked to an arXiv paper they described as fixing the issue; another user praised the work.
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A reply cites the UK and Mistral; the original poster clarifies they meant the EU.
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A post introducing the approach says its two modes combine latent-space thinking with text-based actions in one multi-turn flow.
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Yoshua Bengio says the letter asks AI and security communities to collaborate to protect critical infrastructure and digital systems.
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The author proposes 'fork-and-flush' as a way out of those persistent idea basins.
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OpenAI says it drew on an independent mathematics advisory group’s advice about how to release the results.
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Reflection says Beam uses 23B active parameters and plans to publish its full weights later in October 2026.
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One post asks how that scenario would differ from what happened during COVID.
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arXiv says the change aims to distribute moderator time fairly and support staff, volunteers, readers and authors.
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Bloomberg, citing people with direct access to the effort, reports that Google’s forthcoming model does less well when employees put it to work than it does on industry benchmarks.
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Google announces a 1 million-token output limit, up from 64,000, for its new model. Initial access goes to trusted cyber defenders through the Fairwind Program.
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A post about the project claims a 65% score gain with the same compute on a 24-hour multi-repository agent-swarm task.
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A user is asking for more votes on how one year in the ‘AI age’ compares with ‘normal times.’
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Inherent Labs announced the letter on September 29, saying UK AI companies that have raised $5 billion support its call for people to be free to start and scale businesses.
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A user calls linear mode connectivity one of the most underrated features of training neural networks.
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One post argues authors should use AI reviews to improve their writing, not have models write papers. A reply warns that banning AI-written papers would make arXiv irrelevant.
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The post’s skepticism centers on finding someone with the expertise to evaluate frontier AI, no financial ties to a lab, and the backbone to speak up when things go wrong.