experience-driven-learning

This paper introduces AutoMMemo, a framework that enables multimodal agents to automatically design memory mechanisms (expressible as executable memo programs) for learning from multimodal interaction trajectories, outperforming no-memory and fixed-memory baselines on GUI/Web navigation and visual reasoning benchmarks.

Researchers from Harbin Institute of Technology and Singapore Management University investigate safety risks in experience-driven self-evolving LLM agents, finding that even benign task experience can compromise safety in high-risk scenarios due to agents' execution-oriented tendencies, and revealing a fundamental safety–utility trade-off.