[2603.22306] Memory Bear AI Memory Science Engine for Multimodal Affective Intelligence: A Technical Report

Computer Science > Artificial Intelligence arXiv:2603.22306 (cs) [Submitted on 18 Mar 2026] Title:Memory Bear AI Memory Science Engine for Multimodal Affective Intelligence: A Technical Report Authors:Deliang Wen, Ke Sun, Yu Wang View a PDF of the paper titled Memory Bear AI Memory Science Engine for Multimodal Affective Intelligence: A Technical Report, by Deliang Wen and 2 other authors View PDF HTML (experimental) Abstract:Affective judgment in real interaction is rarely a purely local prediction problem. Emotional meaning often depends on prior trajectory, accumulated context, and multimodal evidence that may be weak, noisy, or incomplete at the current moment. Although multimodal emotion recognition (MER) has improved the integration of text, speech, and visual signals, many existing systems remain optimized for short-range inference and provide limited support for persistent affective memory, long-horizon dependency modeling, and robust interpretation under imperfect input. This technical report presents the Memory Bear AI Memory Science Engine, a memory-centered framework for multimodal affective intelligence. Instead of treating emotion as a transient output label, the framework models affective information as a structured and evolving variable within a memory system. It organizes processing through structured memory formation, working-memory aggregation, long-term consolidation, memory-driven retrieval, dynamic fusion calibration, and continuous memory updating. A...