RSI was missing a constitution. Not “AI improves itself.” Proof-gated evolution: Aim → Act → Prove → Evolve. No proof, no evolution.
No eval, no propagation.
No rollback, no release. AEP-001: GoalOS https://
github.com/MontrealAI/pro
of-gradient/blob/main/docs/standards/AEP-001/GoalOS_Proof-of-Evolution-Constitution_AEP001_v12.1_Institutional_Final.pdf
… #GoalOS #MontrealAI
@montreal_ai
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RSI missing constitution; MontrealAI unveils proof-gated evolution GoalOS
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AlphaProof vs LeanMarathon: proof search vs fidelity maintenance
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The important distinction: AlphaProof-style systems show how AI can search for formal proofs. LeanMarathon asks how an AI system can preserve target fidelity across an entire research-level Lean development. That is a different problem: not just proving, but maintaining a
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LeanMarathon: Building reliable AI co-mathematicians via long proofs
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AI co-mathematicians will not be built by making one prover cleverer. They will be built by making long proofs survive time. A new paper by Yuanhe Zhang, Yuekai Sun, Taiji Suzuki, Jason D. Lee, and Fanghui Liu introduces LeanMarathon: LeanMarathon: Toward Reliable AI
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Canada launches AI for All focusing on trustworthy, efficient AI systems
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Canada just launched AI for All. The mission is clear: access alone will not be enough. Canada now needs AI leverage people can trust — systems that make AI useful, reusable, efficient, and provable. LLMs made intelligence accessible.
The next wave makes intelligence -
AEP-001 GoalOS Proof-of-Evolution Constitution Standard
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New standard for the agent era: AEP-001 — GoalOS Proof-of-Evolution Constitution Commit → Execute → Prove → Evolve. No proof, no evolution.
No eval, no propagation.
No rollback, no release. This is Proof-Carrying Intelligence. https://
montrealai.github.io/proof-gradient
/standards/AEP-001/
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Capabilities learned not inherited: building from ground up
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The phrase “capabilities should be learned, not inherited” is doing a lot of work here. It draws a clear line between imitating intelligence through distillation and building the internal machinery to generate, evaluate, and improve capabilities from the ground up. That
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Microsoft AI’s MAI-Thinking-1: Progress is a model-improving machine
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AI progress is not a model. It is a machine that keeps improving models. That is the core idea behind Microsoft AI’s new technical report: MAI-Thinking-1: Building a Hill-Climbing Machine This is not just a model release. It is a blueprint for turning frontier model
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Proof Gradient: The Agent Evolution Protocol for AI Agents
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Proof Gradient · The Agent Evolution Protocol One agent tries.
Proof decides.
The network evolves. GoalOS gives the network Direction.
PlanOS gives it Strategy.
SkillOS gives it Capability.
The Proof Gradient gives it Evolution. https://
montrealai.github.io/proof-gradient/ #AGIALPHA #Jobs -
Token-level SSL vs Latent Prediction: Climbing Abstraction Hierarchies
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The key distinction: Token-level SSL asks the model to recover the hidden tree through the leaves. Latent prediction lets the model climb the tree. Once one abstraction level is learned, it becomes the substrate for learning the next.
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New paper on sample-complexity theory for data-efficiency gap
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The data-efficiency gap between machines and children may not be solved by “more tokens.” It may be solved by changing what the model is asked to predict. A beautiful new paper by Daniel J. Korchinski, Alessandro Favero, and Matthieu Wyart gives a sample-complexity theory for a