A multimodal agent AI survey maps what the subfield has actually established — and how much of it is still position papers
A survey of recent advances in multimodal agent AI, published through JCST and the ACM DL, catalogues the subfield's results and directions. Surveys are most useful for what they reveal about the ratio of established findings to proposals.
Multimodal agents have accumulated an unusual amount of architecture proposal relative to replicated result. That is characteristic of a subfield moving faster than its evaluation methodology, and a survey is the natural place for that imbalance to become visible.
Read alongside MemCtrl's learned-forgetting approach and AMA's maintenance framing, the memory strand looks like the one currently producing testable claims rather than frameworks.
JCST — Multimodal Agent AI: A Survey of Recent Advances and Future Directions → · ACM Digital Library — Multimodal Agent AI: A Survey →