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

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.

Figures (11)

• Figure 2.1: Motivational comparison between conventional local affective inference and memory-centered affective inference. Conventional multimodal systems typically rely on current-turn text, audio, and visual cues to produce a local emotion label, whereas memory-centered inference integrates current input with prior interaction history and affective memory to produce a context-calibrated judgment.
• Figure 3.1: Conceptual shift from snapshot-based multimodal emotion recognition to a memory-centered framework for persistent affective understanding.
• Figure 4.1: Overall architecture of the Memory Bear AI framework. The system processes multimodal inputs through preprocessing, memory formation, consolidation, retrieval, memory-guided fusion, and affective decision-making, while continuously updating memory based on new interaction evidence.
• Figure 4.2: The four-stage architecture of the memory-driven affective engine.
• Figure 4.3: Detailed architecture of the multimodal representation learning pipeline.