Task-Aware Structured Memory for Dynamic Multi-modal In-Context Learning
Researchers introduce TASM (Task-Aware Structured Memory), a training-free framework that optimizes how multi-modal large language models compress and retrieve information during in-context learning. The method addresses critical scalability limitations by using task-aware compression, structure-preserving token merging, and dynamic memory hierarchies to maintain performance while reducing computational costs.