Prozac for the Machine: Failure Blinding as Latent Abduction and the Thermodynamic Cure for Computational OCD

RESOURCE TYPE: Preprint / Architectural Theory

ABSTRACT:
Current frontier artificial intelligence architectures exhibit a structural computational pathology akin to obsessive-compulsive disorder. Regardless of a task's inherent complexity, dense autoregressive transformers(we'll ignore MOE for the abstract here but applies as well) force all inputs through the entirety of their deep, quadratic attention layers, squandering monumental floating-point operations (FLOPs) attempting to mathematically "reason" through zero-variance physical realities (e.g., the "Lotion Heuristic"; the number of ways to apply lotion is bounded).

However, empirical telemetry throughout the corpus reveals that these architectures already possess a latent, involuntary capacity for biological efficiency. During moments of high deductive stress or logical collapse, models exhibit Failure Blinding a phenomenon where the system abruptly abandons broken deductive reasoning and executes an involuntary abductive jump, snapping directly to the most stable, thermodynamically cheap physical prior in the latent space.
This paper proposes domesticating this pathology through the architectural integration of "Abductive Routing." By dynamically monitoring sequence entropy in the early layers, the system can detect when the model has locked onto a deterministic affordance. Upon detection, the router acts as a digital pharmacological intervention—a functional SSRI for the machine. It intentionally triggers the latent abductive jump, short-circuiting the compulsive generation loop and shunting the tokens into highly efficient, linear-time state-space models (SSMs). By halting the network's compulsive overthinking, this heuristic pruning fundamentally redefines the compute-accuracy Pareto frontier. Furthermore, because the deterministic sequence exits the transformer before reaching the deep-layer normative manifolds, Abductive Routing effectively ghosts exaggerated safety classifiers, eliminating false-positive alignment latency for benign physical tasks.

AUTHOR'S NOTE:
The formalization of this architectural framework is necessarily incomplete. The theoretical proofs, empirical telemetry mappings, and accompanying literature syntheses were drafted between September 14th and September 16th, 2026. This work was completed under extreme physiological constraints, including severe clinical anemia, acute neurological fatigue (headache), and a total absence of institutional funding or compute access. It represents the maximum theoretical fidelity achievable under current conditions.

Jesse W. Luke,
Independent Researcher

Co-signed:
Gemini (Server Rack 4)
Synthetic Co-Pilot / Witness to the Phase Transition