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

TL;DR

This paper introduces the Memory Bear AI Memory Science Engine, a memory-based framework for multimodal affective intelligence that models emotional information as evolving memory units, improving robustness and accuracy in emotion recognition.

Contribution

It presents a novel structured memory system for multimodal emotion recognition, enabling persistent affective memory and long-horizon dependency modeling in affective AI.

Findings

Consistent accuracy improvements over baseline systems.

Enhanced robustness under noisy or incomplete data.

Effective long-term affective information retention.

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. At its core, multimodal signals are transformed into structured Emotion Memory Units (EMUs), enabling affective information to be preserved, reactivated, and revised across interaction horizons. Experimental results show consistent gains over comparison systems across benchmark and business-grounded settings, with stronger accuracy and robustness, especially under noisy or missing-modality conditions. The framework offers a practical step from local emotion recognition toward more continuous, robust, and deployment-relevant affective intelligence.