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

2026-03-25 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, extended

Summary

The Memory Bear AI Memory Science Engine is a novel framework for multimodal affective intelligence that models emotional information as a structured, evolving variable within a memory system, rather than a transient output label. This architecture integrates multimodal processing through structured memory formation, working-memory aggregation, long-term consolidation, memory-driven retrieval, dynamic fusion calibration, and continuous memory updating. It transforms multimodal signals into "Emotion Memory Units" (EMUs) to preserve, reactivate, and revise affective information across interactions. The framework consistently outperforms comparison systems, achieving 78.8% and 66.7% accuracy on IEMOCAP and CMU-MOSEI, respectively. On the Memory Bear AI Business Dataset, it reaches 68.4% accuracy, 48.6% weighted F1, and 45.9% macro F1, improving accuracy by 8.2 points over a traditional fusion baseline. It also demonstrates superior robustness under degraded multimodal conditions, retaining 92.3% of its complete-condition performance.

Key takeaway

For NLP Engineers and AI Scientists developing multimodal emotion recognition systems, you should consider integrating a memory-centered architecture. This approach, exemplified by the Memory Bear AI engine, significantly enhances robustness and accuracy in real-world scenarios with noisy, incomplete, or context-dependent affective signals, moving beyond snapshot-based predictions to more stable, continuous affective understanding. Your systems will benefit from explicit memory management, including structured encoding, selective retention, and memory-guided fusion.

Key insights

Affective intelligence benefits from a memory-centered approach that models emotion as an evolving, structured memory variable.

Principles

• Emotional understanding must be history-aware.
• Present multimodal interpretation should be memory-calibrated.
• Affective memory must be selectively consolidated, prioritized, and forgotten.

Method

The Memory Bear AI engine processes multimodal inputs through representation learning, structured affective memory modeling, dynamic fusion strategies, and classification with continuous memory updating.

In practice

• Use Emotion Memory Units (EMUs) to structure affective data.
• Implement selective consolidation for long-term memory.
• Calibrate multimodal fusion with retrieved historical memory.

Topics

• Memory Bear AI Engine
• Multimodal Affective Intelligence
• Emotion Memory Units
• Dynamic Fusion
• Long-Horizon Memory

Best for: NLP Engineer, AI Scientist, Research Scientist, AI Researcher, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.