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Losing the Thread

Autoregressive Drift in Generative AI and What Comes Next.

First Principals: Why fluent generative systems lose objective fidelity across long outputs, agentic workflows, and model scale—and why generation-time control becomes necessary.

17 articles Presented in reading order
  1. Objective Pursuit Is Not Objective Origin 01
    Article 1

    Objective Pursuit Is Not Objective Origin

    The current AI discussion often conflates two capacities: pursuing an objective and originating one. Clarifying this difference is essential.

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  2. Parsing Intelligence: From Maslow to Tinbergen 02
    Article 2

    Parsing Intelligence: From Maslow to Tinbergen

    Once the distinction between objective pursuit and objective origin is established, the next problem is explanatory discipline.

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  3. From Hierarchy to Control 03
    Article 3

    From Hierarchy to Control

    What happens when a system begins to act over time within a structure of objectives it did not fully originate?

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  4. What the Transformer Actually Changed 04
    Article 4

    What the Transformer Actually Changed

    The transformer gave modern AI a far better engine. It did not, by that fact alone, provide a steering system.

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  5. Fluent Failure: Why Capable Models Drift Smoothly 05
    Article 5

    Fluent Failure: Why Capable Models Drift Smoothly

    Probability, Weights, and the Logic of Local Continuation

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  6. Decoding Is a Control Surface 06
    Article 6

    Decoding Is a Control Surface

    Where Capability Becomes Behavior

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  7. Drift, Named 07
    Article 7

    Drift, Named

    How Local Continuation Loses the Global Objective

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  8. Why Long Tasks Break More Than Short Ones 08
    Article 8

    Why Long Tasks Break More Than Short Ones

    Length Is Not Just More Output

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  9. The Current Toolkit and Its Limits 09
    Article 9

    The Current Toolkit and Its Limits

    Prompts, Retrieval, Fine-Tuning, and Review

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  10. When Agents Inherit Drift 10
    Article 10

    When Agents Inherit Drift

    From Generated Text to Generated Action

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  11. Bigger Models, World Models, Same Problem 11
    Article 11

    Bigger Models, World Models, Same Problem

    Scale Improves Capability. It Does Not Eliminate Drift.

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  12. The Case for Runtime Control 12
    Article 12

    The Case for Runtime Control

    Why Reliability Has to Happen During Generation

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  13. Equilibrium-Constrained Decoding: Holding the Thread 13
    Article 13

    Equilibrium-Constrained Decoding: Holding the Thread

    A Runtime-Control Approach to Objective Fidelity

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  14. The ε Series as a Governance Artifact 14
    Article 14

    The ε Series as a Governance Artifact

    Making Objective Fidelity Observable

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  15. The Economics of Objective Retention 15
    Article 15

    The Economics of Objective Retention

    Governable Scale and the Cost of Trust

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  16. Magnifica Humanitas and the Question AI Cannot Answer for Itself 16
    Article 16

    Magnifica Humanitas and the Question AI Cannot Answer for Itself

    What Technology Is Made to Serve

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  17. On the Regress Objection: Why ECD Moves the Alignment Problem to a Place Institutions Already Know How to Govern 17
    Article 17

    On the Regress Objection: Why ECD Moves the Alignment Problem to a Place Institutions Already Know How to Govern

    Final coda to Losing the Thread.

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