Perceiver IO: a scalable, fully-attentional model that works on any modality

Perceiver IO extends the original Perceiver architecture with a flexible output mechanism — any modality (images, audio, text, point clouds) maps through cross-attention to a fixed latent array, then decodes to arbitrary output shapes via a learned output query.

Teams running separate ViT + Whisper + text embedder stacks in multimodal RAG pipelines are paying 3-4x the inference cost they need to. Most developers default to modality-specific models out of habit, not because they benchmarked the alternative.

Multimodal agent builders managing more than two input types should benchmark Perceiver IO as a unified backbone. Text-only RAG teams can skip this entirely.