Autoregressive Hallucinations and Global Constraints
lnsip9reg · reddit · 2026-07-12
The author presents a lengthy piece comparing the commonalities between autoregressive generation and N-body numerical simulations: both compress a strongly coupled, nonlinear system into "step-by-step updated" local computations, inevitably leading to recursive error amplification, long-term drift, and instability.
The core argument is that AI "hallucinations" are not merely issues of training data or model capacity, but rather structural problems of sequential modeling. Borrowing the concept of Lyapunov time from dynamical systems, the author explains that a tiny deviation at step N will propagate through subsequent steps, causing global consistency to gradually collapse while local outputs still appear reasonable.
The author further argues that methods like RLHF, prompt engineering, and guardrails are merely "patching after the fact" and cannot fundamentally solve the drift. A more fundamental approach is global solving/field-style computation, constraining the overall state simultaneously rather than assembling consistency step-by-step. The article concludes by proposing the Tesseract-Symmetry Engine (TSE) as a direction for implementing this concept.
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