NEWBURGH, Ind. - TelAve -- Catchproof today announced the publication of A Stability Envelope Is All You Need, a landmark research paper that challenges one of the most persistent assumptions in modern AI: that transformer inference is a deterministic readout of fixed weights.

The paper demonstrates that this assumption is structurally incorrect.

Instead, it introduces Stable Latent Propagation (SLP)—a mechanism that reveals inference to be a stability‑bounded, path‑dependent process governed by an underlying stability envelope, much like training. This correction reframes how the field understands transformer behavior, failure modes, and the limits of inference‑time reliability.

"The field has been reasoning about inference using conceptual tools built for deterministic systems," said Barbara Roy, founder of Catchproof and author of the study. "But modern AI is non‑deterministic, non‑stationary, and stateful. Once you model inference as a stability‑bounded process, the failure modes stop looking mysterious and they become predictable."

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Key Findings
The paper shows that well‑known issues such as:

• hallucination
• context drift
• chain‑of‑thought instability
• long‑range collapse
• pseudo‑agency
• context fragmentation

…are not anomalies or quirks of prompting. They are structural consequences of operating outside the inference stability envelope.

By formalizing how latent representations propagate across sequential evaluation steps, the paper establishes the foundation for a new discipline of inference‑layer stability—a layer that has been missing from AI systems thinking.

Why This Matters
For years, the field has focused on:

• architecture (Attention)
• scaling (GPT‑3)
• retrieval (RAG)
• alignment (RLHF)
• reasoning hacks (Chain‑of‑Thought)
• tool use (ReAct)

But none of these frameworks explain why inference becomes unstable, why reasoning collapses, or why long‑context behavior degrades.

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This paper provides the missing structural model.

A New Discipline for AI Systems
The Stability Envelope paper is the first in a series of works from Catchproof that introduce a discipline‑based approach to AI system behavior. Two companion papers—Stable‑State Responsive Alignment and Misinterpretation in Autonomous Systems—extend the framework into alignment and agent diagnostics.

Together, these works offer a new foundation for understanding and governing modern AI systems at the level where failures actually occur: the inference substrate.

Availability
The full paper is available at: https://catchproof.square.site/papers