Author

Kangsan Kim

KAIST

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#artificial intelligence Preprint Oct 2026

Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory

Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities. However, enabling multimodal agents to continually acquire useful information from complex, heterogeneous multimodal data remains a signif...

Ming-Hao Guo, Qing-Yue Jiao, Xiu-Jin Liu et al. · 0 citations

Preprint Aug 2026

Agent Memory Distillation: Empowering Small LLM Agents with Hierarchical Teacher Memory

Agent Memory Distillation (AMD), a training-free framework that transfers structured knowledge from a large teacher agent to a small student agent through hierarchical memory, is proposed, which consistently outperforms existing memory-based baselines.

Taeil Kim, Kangsan Kim, S. Hwang · 3 citations