Exactly. 'Impossible to unprison' is the wrong goal. The true primitive is authority protected by proof. GoalOS assumes that every model, agent, and evaluator can fail: generate → verify → contest → canary → monitor → cancel The capacity
@montreal_ai
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The Canonical Affirmation: The Six Steps of the GoalOS Cycle
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The canonical affirmation: 1. GoalOS defines what must change.
2. Agents attempt the change.
3. Evidence demonstrates what happened.
4. Evaluation determines what worked.
5. The Proof Gradient determines what can propagate.
6. The Register -
GoalOS Mission OS: The Proof OS for Autonomous AI Work
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AI does not need to produce more output. It needs to produce proof. I’m sharing my paper: GoalOS Mission OS: The Proof OS for Autonomous AI Work The thesis is simple: AI creates output.
GoalOS creates proof. Today’s models can answer.
Agents can act.
But institutions need -
GoalOS AGIALPHA Ascension: Experimental Framework for Persistent and Self-Improving AI
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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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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
… #AGIALPHA #AGIAscension -
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