AMES: Approximate Multi-modal Enterprise Search via Late Interaction Retrieval
We present AMES (Approximate Multimodal Enterprise Search), a unified multimodal late interaction retrieval architecture which is backend agnostic. AMES demonstrates that fine-grained multimodal late interaction retrieval can be deployed within a production grade enterprise search engine without architectural redesign. Text tokens, image patches, and video frames are embedded into a shared representation space using multi-vector encoders, enabling cross-modal retrieval without modality specific retrieval logic. AMES employs a two-stage pipeline: parallel token level ANN search with per document Top-M MaxSim approximation, followed by accelerator optimized Exact MaxSim re-ranking. Experime...
| Authors | Tony Joseph, Carlos Pareja, David Lopes Pegna et al. |
|---|---|
| Primary category | cs.IR |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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