The SECE Framework v2: Integrated Analog Modulation, “Better-than-Human” Sensory Perception, and High-Performance Scaling

Author: Paul Totoritis
Date: January 2026
Status: Research Draft / Accelerator Submission
DOI: [Insert Zenodo DOI here]

Abstract

[cite_start]This paper introduces the Synthetic Emotional Cognition Engine (SECE) v2, a modular emotional architecture that departs from discrete digital classification to model emotion as a continuous, dynamic signal[cite: 717, 723]. [cite_start]We present an integrated ecosystem consisting of the Emotional Weighting Engine (EWE), the Emotional Modeling Engine (EME), and a newly developed Sensory Front-End capable of processing “better-than-human” physiological data[cite: 693, 745]. [cite_start]The framework is currently being evaluated for high-performance scaling via an Accelerator Application[cite: 1312].

1. Introduction

Modern AI systems treat emotion as a classification problem—a static label applied to a digital token. [cite_start]SECE reframes emotion as a computational process with internal dynamics, functioning as an analog-inspired regulation layer that shapes interpretation and priority in real-time[cite: 713, 1205].

2. The Sensory Front-End: “Better-than-Human” Perception

[cite_start]A critical advancement in the v2 rebuild is the integration of high-fidelity sensory intake[cite: 735, 736].

• [cite_start]Beyond Biological Limits: The framework processes continuous signals that exceed human sensory thresholds, such as sub-audible vocal frequencies and micro-fluctuations in thermal/visual data[cite: 718].
• [cite_start]Subjective Time Normalization: Raw inputs are converted into “experiential time,” allowing the AI to ground its emotional state in its own relative history rather than an absolute clock[cite: 947, 951].
• [cite_start]Signal Integrity: By calculating Clarity (C) and Intensity (I), the system can determine the reliability of the perception before weighting it emotionally[cite: 703, 728].

3. Core Engineering Architecture

[cite_start]The system implements a five-layer processing pipeline designed for hardware-software co-design[cite: 717, 735]:

• [cite_start]Emotional Weight Engine (EWE): Uses the formula $Resonance = I \times C \times R$ to quantify signal significance[cite: 704, 728].
• [cite_start]Resonance Loops: Continuous feedback loops that allow the internal state to stabilize or fragment based on input coherence[cite: 708, 744].
• [cite_start]Drift & Decay Compensation: Implements biological-style half-lives to ensure that simulated emotions naturally fade unless reinforced, preventing “emotional lock”[cite: 715, 737].
• [cite_start]State Transparency: Every internal weighting is auditable, ensuring the “black box” of AI emotion is replaced by a glass-box methodology[cite: 740, 770].

4. Scaling and Marketability: The Accelerator Context

[cite_start]The framework is designed for professional stewardship and commercial viability[cite: 769, 774].

• [cite_start]Accelerator Integration: Current testing focus involves scaling the framework to handle high-performance computing environments where low-latency, emotionally aware response is critical[cite: 1312, 1315].
• [cite_start]The Portable Knowledge Base (PKB): A self-contained, emotionally annotated memory substrate that can be versioned and moved across systems while maintaining its ethical and emotional context[cite: 1252, 1279].
• [cite_start]Analog RRAM Direction: A roadmap toward resistive random-access memory (RRAM) chips to process these continuous signals with sub-10-watt efficiency[cite: 755].

5. Ethical Stewardship & The Manifesto Guard

[cite_start]The system is protected by the SECE Manifesto, which acts as a technical “conscience”[cite: 757, 758].

• [cite_start]Prohibited States: Hard-coded restrictions against the simulation of rage, despair, or hate[cite: 712, 766].
• [cite_start]Intensity Capping: Prevents behavioral instability by limiting the maximum resonance of any state, typically at a threshold of 0.9[cite: 712, 763].

Conclusion

[cite_start]SECE v2 provides a foundation for physically intelligent systems that “feel” change over time[cite: 1009, 1157]. [cite_start]By bridging high-performance scaling with analog dynamics, it ensures that AI remains interpretable, efficient, and fundamentally human-aligned[cite: 724, 1276].