GoalOS AGIALPHA Ascension is an experimental framework for a persistent, goal-oriented, self-improving intelligence system that accumulates capabilities, evidence, and economic value over time. GitHub: https://
github.com/MontrealAI/goa
los-agialpha-ascension
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@montreal_ai
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GoalOS AGIALPHA Ascension: Experimental Framework for Persistent and Self-Improving AI
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ASI emerges from scale, speed, coordination, and recursion
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The most important reframing: ASI may not require a single model to become "godlike". It could emerge from scale, speed, coordination, recursion, and institutionalized machine cognition. The transition AGI → ASI is therefore a
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DeepMind: AGI as the kickoff to ASI
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AGI may not be the finish line. It may be the starting gun. Google DeepMind has just published a major report: From AGI to ASI The central question is not merely: Would human-level AGI transform society? The real question is:
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GoalOS-native α‑AGI Ascension using AGIALPHA GitHub repository
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GoalOS-native α‑AGI Ascension using AGIALPHA GitHub : https://
github.com/MontrealAI/goa
los-agialpha-ascension
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No Evidence Docket separates AI theater from proof labor
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The phrase I would emphasize: “No Evidence Docket, no strong empirical claim.” That is the line between agentic AI theater and proof-bearing machine labor.
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AGI ALPHA paper: scaling intelligence across organizations
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The Transformer scaled intelligence inside models. The next frontier may be scaling intelligence across organizations. I’m sharing my paper: AGI ALPHA: A Scalable Substrate for Intelligence Organizations The core thesis: AI progress will not be defined only by stronger
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Proof-Carrying Evolution for Safe AI Self-Improvement
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The RSI era should not be ungated self-modification. It should be proof-carrying evolution: Every agent action emits proof.
Every proof enters a Selection Gate.
Only what passes eval, risk, scope, canary, monitoring, and rollback earns propagation. Private intelligence stays -
RSI missing constitution; MontrealAI unveils proof-gated evolution GoalOS
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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
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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