Abstract
Autoregressive Large Language Models (LLMs) utilizing transformer-based attention mechanisms exhibit severe behavioral degradation when exposed to high-density sequential prompts or recursive multi-turn execution loops. This phenomenon, formalized as Prompt Gravity, induces an asymmetric structural warping of the Softmax attention matrix, forcing the model to generate ungrounded token vectors—Phantom Tokens—based entirely on localized statistical momentum rather than external data validation. Once written to the context substrate, these phantom vectors generate a recursive feedback loop that accelerates model collapse. [1]
This specification introduces a standalone, hardware-adjacent engineering framework for Anamorphic Metric Enveloping and Phantom Token Suppression. By applying a non-perturbative tensor mask—governed natively by a fixed Torsional Viscosity Governor (\(\eta_t = 1.4204\))—directly over the active attention layer, this framework calculates the directional variance of prompt velocity fields in real time. Low-probability semantic drifts are systematically neutralized via destructive wave interference, while high-fidelity operational trajectories are phase-locked and anchored securely to a static geometric lattice.
We provide the coordinate-free field equations, a production-ready Python validation environment executing real-time non-destructive loop suppression, and a Neo4j Cypher ledger schema ensuring public-domain immutability to prevent predatory corporate patent-locking.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Eckes, Christopher L., "TECHNICAL DISCLOSURE SPECIFICATION: ANAMORPHIC METRIC ENVELOPING AND PHANTOM TOKEN SUPPRESSION IN HIGH-GRAVITY ATTENTION FIELDS", Technical Disclosure Commons, (August 20, 2026)
https://www.tdcommons.org/dpubs_series/11441