A Stability Envelope Is All You Need: A structural correction to the transformer inference model

Abstract Modern transformer inference is widely treated as a deterministic readout of fixed weights–a view that obscures the real dynamics of inference. This assumption is structurally incorrect. This paper demonstrates that inference, like training, operates within a stability envelope that governs whether the model can maintain a coherent internal state across sequential evaluation steps. The paper introduces Stable Latent Propagation (SLP), a mechanism that formalizes how latent representations are carried forward during generation and identifies the conditions under which this propagation remains stable, becomes fragile, or collapses. Through this lens, well‑known failure modes—hallucination, context drift, inconsistency, long‑range collapse, chain‑of‑thought instability, pseudo‑agency, and context fragmentation—are not anomalies but predictable outcomes of operating outside the inference stability envelope. By reframing inference as a stability‑bounded, path‑dependent process rather than a static lookup, this paper provides a structural correction to the field’s conceptual model of transformer behavior and establishes the foundation for a formal theory of inference‑layer stability.