A systems design diagnosis of hallucination, corrigibility, and the structural gap that scaling cannot close
The post The Inversion Error: Why Safe AGI Requires an Enactive Floor and State-Space Reversibility appeared first on Towards Data Science.
The Inversion Error: Why Safe AGI Requires an Enactive Floor and State-Space Reversibility
Towards Data Science / 4/2/2026
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Key Points
- The article argues that simply scaling models cannot resolve core safety gaps such as hallucination behavior, corrigibility, and structural misalignment issues.
- It introduces the concept of an “inversion error,” claiming that today’s learning and inference setups fail to guarantee reliable, reversible mapping between internal representations and real-world states.
- The author proposes that safe AGI requires an “enactive floor,” emphasizing grounded, action-perception coupling and embodied/interaction-based constraints rather than purely feedforward learning.
- The piece calls for “state-space reversibility” as a structural requirement, aiming to make it possible to trace and correct how an agent’s internal state corresponds to external outcomes.
- Overall, it presents a systems-design diagnosis framing AGI safety as a representation-and-control architecture problem rather than a parameter scaling problem.
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