Science has a hidden frontier. Not the frontier of what is true. The frontier of what is thinkable. A remarkable new preprint by Alejandro H. Artiles, Martin Weiss, Levin Brinkmann, Iyad Rahwan, Bernhard Schölkopf, Christopher Pal, Hugo Larochelle, Anirudh Goyal, and Nasim
@ceobillionaire
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TRINITY: 0.6B Model Managing Giants (ICLR 2026)
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A 0.6B model learned to manage giants. That is the idea behind TRINITY, a new ICLR 2026 paper by Jinglue Xu, Qi Sun, Peter Schwendeman, Stefan Nielsen, Edoardo Cetin, and Yujin Tang. The paper is not asking: “How do we build one model that knows everything?” It is asking
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Montreal.AI YouTube: AGI Debate Archive Relaunch
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The archive is awake. Introducing the renewed http://
MONTREAL.AI YouTube channel — public intelligence for the AGI‑First → ASI‑First era. Home of the AGI Debate archive and the official video record for http://
MONTREAL.AI & http://
QUEBEC.AI. -
Paper: Conversational Interfaces as a New Biology Interface
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The next interface to biology may not be a dashboard.
It may be a conversation. I just read a new preprint by Yanbo Zhang and Michael Levin that feels like it belongs in the “this may open an entirely new category” folder. The paper is called: “Language Game: Talking to -
Positive Alignment: Artificial Intelligence for Human Flourishing
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Positive Alignment: Artificial Intelligence for Human Flourishing Laukkonen et al.: https://
arxiv.org/abs/2605.10310 #ArtificialIntelligence #AIAgents -
Causal Emergence Alignment Hypothesis in Reinforcement Learning Agents
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The Causally Emergent Alignment Hypothesis: Causal Emergence Aligns with and Predicts Final Reward in Reinforcement Learning Agents Federico Pigozzi, Michael Levin: https://
arxiv.org/abs/2605.06746 #AIAgents #ReinforcementLearning -
Research on deep information propagation and symmetry breaking in neural networks
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Spontaneous symmetry breaking and Goldstone modes for deep information propagation Iqbal et al.: https://
arxiv.org/abs/2605.14685 #ArtificialIntelligence #DeepLearning #AIAgents -
AGI Alpha: A Public Proof Layer for Self-Improving AI
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Recursive validated self-improving AI at $ 4.65B. AGI ALPHA is building the public proof layer. Not just AI that improves—
AI that proves, replays, audits, archives & compounds. Proof-bound. Enterprise-ready. Built in public. https://
github.com/MontrealAI/agi
alpha-first-real-loop
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LLMs Improving LLMs: New Research on Agentic Discovery and Scaling
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LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling Zheng et al.: https://
arxiv.org/abs/2605.08083 #ArtificialIntelligence #DeepLearning #AIAgents -
New entities emerging in the AGI development space
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Recursive is emerging into the category. AGI ALPHA has already been building the category in public. #AGIALPHA #MontrealAI #QuebecAI