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
@ceobillionaire
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Paper: Semantic Hierarchies Are Geometric in Language Models
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What looks like ontology may be eigenspectrum. A beautiful new paper by Andres Nava and Matthieu Wyart gives a mechanistic account of one of the most striking facts about language models: semantic hierarchies appear geometrically. An owl is a bird.
A bird is an animal.
An -
Microsoft paper: SkillOpt for self‑evolving agent skills
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One of the most important objects in agentic AI may turn out to be a Markdown file. Not the model weights.
Not the prompt. The skill document. A new Microsoft paper introduces SkillOpt: Executive Strategy for Self-Evolving Agent Skills. The thesis is sharp: If an agent’s -
Calibration vs. Discrimination in Model Uncertainty
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The calibration vs. discrimination distinction is crucial. A model can know its average error rate without knowing which particular answer is wrong. That is why “just abstain when uncertain” is not enough — poor discrimination creates a utility tax. Faithful uncertainty is a
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Paper argues metacognition may reduce AI hallucinations
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Trustworthy AI may not require omniscience. It may require epistemic honesty. A new paper by Gal Yona, Mor Geva, and Yossi Matias makes one of the clearest arguments I’ve seen for why hallucinations remain hard — and why the path forward may be metacognition. Hallucinations
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When extra test-time compute helps model convergence
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The key distinction: More test-time compute is not automatically useful. It becomes useful when the model has learned an internal landscape where extra iterations move the latent state toward solution-aligned attractors rather than spurious ones. That is why the convergence
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New paper introduces Equilibrium Reasoners for latent AI reasoning
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The next clue in AI reasoning: answers may be attractors. A new paper from Benhao Huang, Zhengyang Geng, and Zico Kolter introduces Equilibrium Reasoners (EqR) — a sharp mechanistic view of test-time scaling in latent reasoning models. The core idea is simple, but deep:
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ConvexTok vs BPE: moving tokenization toward optimality
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The key distinction: BPE gives us a strong procedure. ConvexTok gives us a procedure, an optimization relaxation, and a certificate. That moves tokenisation from engineering folklore toward measurable optimality.
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Paper: Tokenisation via Convex Relaxations reframes tokenizers
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The tokenizer is an architectural prior disguised as preprocessing. And almost everyone has been treating it like plumbing. A new paper by Jan Tempus, Philip Whittington, Craig W. Schmidt, Dennis Komm, and Tiago Pimentel changes the frame: Tokenisation via Convex Relaxations
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Montreal.AI launches public forum for AGI-first to ASI-first era
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MONTRÉAL.IA / http://
MONTREAL.AI is live on Eventbrite. Public Intelligence for the AGI-first → ASI-first Era. Briefings. Debates. Archives. Public record. http://
MONTREAL.AI convenes the public-intelligence forum for frontier AI, sovereign intelligence,