WorldMemArena: Evaluating Multimodal Agent Memory Through Action-World Interaction 事件

WorldMemArena: Evaluating Multimodal Agent Memory Through Action-World Interaction arXiv:2605.29341v1 Announce Type: new Abstract: Multimodal large language models are increasingly deployed as long-horizon agents, where memory must do more than recall: it must track an evolving world, revise what has gone stale, and surface the right evidence at decision time. Existing benchmarks measure recall over static dialogue, collapse memory into a single end-of-task accuracy, and reduce visual observati