Psywarp: A Multimodal Cognitive Framework for Modeling Human Emotion and Behavior

This record contains the full theoretical research paper titled
“Psywarp: A Multimodal Cognitive Framework for Modeling Human Emotion and Behavior.”

Psywarp proposes a modular cognitive AI architecture that integrates multimodal perception, emotional drift modeling, and personality-aware reasoning to simulate human-like cognitive and behavioral processes. The framework introduces conceptual components such as TraitNet, the Emotion-Weighted Cognitive Loop (EWCL), Emotional Drift Modeling (EDM), the Self-Adaptive Decision Engine (SADE), and an Open-Ended Prompt Engine (OEPE).

This work is purely theoretical in nature and does not present empirical experiments, datasets, or implementation results. Its primary objective is to provide a conceptual and architectural foundation for future research in emotionally adaptive and ethically aligned artificial intelligence systems.

Potential application domains include education, mental health, defense psychology, and human–AI interaction.