📄 中文摘要
情感判断在真实交互中往往不是一个纯粹的局部预测问题。情感意义通常依赖于先前的轨迹、累积的上下文以及当前时刻可能较弱、嘈杂或不完整的多模态证据。尽管多模态情感识别（MER）在文本、语音和视觉信号的整合方面取得了进展，但许多现有系统仍然优化于短期推理，且对持久的情感记忆、长时间依赖建模和在不完美输入下的稳健解释支持有限。记忆熊 AI 记忆科学引擎提出了一种以记忆为中心的多模态情感智能框架，旨在克服这些局限性，增强情感理解的深度和广度。
记忆熊 AI 记忆科学引擎用于多模态情感智能：技术报告
📄 English Summary
Memory Bear AI Memory Science Engine for Multimodal Affective Intelligence: A Technical Report
Affective judgment in real interactions is rarely a purely local prediction problem. Emotional meaning often depends on prior trajectories, accumulated context, and multimodal evidence that may be weak, noisy, or incomplete at the current moment. While multimodal emotion recognition (MER) has advanced 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. The Memory Bear AI Memory Science Engine presents a memory-centered framework for multimodal affective intelligence, aiming to address these limitations and enhance the depth and breadth of emotional understanding.