Language models may not need longer context. They may need sleep. A fascinating new paper by Sangyun Lee, Sean McLeish, Tom Goldstein, and Giulia Fanti proposes one of the most biologically resonant ideas in long-context AI: sleep-like memory consolidation. The problem is
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Scalable Memory vs. Reasoning in AI Models
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The key distinction: scalable memory ≠ scalable reasoning. A model can store evicted context in fixed-size fast weights and still fail if it has not spent enough computation transforming that context into a useful state. That is why the “sleep” phase is interesting: it moves
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Sleep-like memory consolidation for AI models
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Language models may not need longer context. They may need sleep. A fascinating new paper by Sangyun Lee, Sean McLeish, Tom Goldstein, and Giulia Fanti proposes one of the most biologically resonant ideas in long-context AI: sleep-like memory consolidation. The problem is
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Google Gemini Projects and Workflow Agents for Teams
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GOOGLE 🔥: Gemini for Business will get a new experience for collaborative Projects, where teams can work in a shared environment.
— 🚨 AI News | TestingCatalog (@testingcatalog) 27 mai 2026
Besides that, Google is rolling out Workflow Agents that can work on automation tasks across various apps. The same functionality is now available… pic.twitter.com/kkVRgh4F14GOOGLE : Gemini for Business will get a new experience for collaborative Projects, where teams can work in a shared environment. Besides that, Google is rolling out Workflow Agents that can work on automation tasks across various apps. The same functionality is now available
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Codex for real-time meeting transcription and Q&A
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Codex for transcribing and answering questions about a meeting in real time:
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Gary Marcus reaffirms his neuro-symbolic AI stance from 2001
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J'ai en effet dit tout cela, et j'ai littéralement des milliers de preuves, remontant à mon livre de 2001 et à mon célèbre article *Deep Learning is Hitting a Wall* qui plaidait fortement pour compléter l'apprentissage profond avec des outils neuro-symboliques. Veuillez lire mon
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Lyft Enhances AI Agent Development with LangGraph and LangSmith
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@Lyft accelerated agent development from 6 months to just a few weeks with LangGraph and LangSmith. Hallucinations decreased by 20% AI Resolution rate up by 16%
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Lyft’s AI Assist: How Ops Teams Ship Agents and Iterate with Prompts
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Today, Ops teams, VoC leads, and PMs are now writing prompts, shipping agents, and iterating. No MLEs required. Read @Lyft
’s guest blog to see how they improved AI Assist, why they treated prompts like product specs rather than code comments, + what’s next. -
Spatial RAG for Geographic AI Model Accuracy
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Yup, the bridge is what I was actually testing to see if it could reproduce. In some rolls it gets close but given a bit of spatial RAG (say of local aerial and street view imagery) the result could be far more geographically accurate.
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Functional vs Distributional Geometry in Hierarchical Concept Spectral Analysis
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The key distinction here is subtle but important: Functional geometry asks what a representation can do. Distributional geometry asks where that representation came from. This paper shows that at least part of hierarchical concept geometry can be explained by the spectral