LUMA-RAG: Lifelong Multimodal Agents with Provably Stable Streaming Alignment
This addresses the challenge of building practical, production-ready multimodal RAG systems for AI agents handling dynamic data streams, representing a strong incremental improvement with specific gains.
The paper tackled the problem of maintaining index freshness and cross-modal consistency in retrieval-augmented generation (RAG) systems for continuous multimodal streams, achieving robust text-to-image retrieval with Recall@10 = 0.94 and provably stable audio-to-image rankings with Safe@1 = 1.0.
Retrieval-Augmented Generation (RAG) has emerged as the dominant paradigm for grounding large language model outputs in verifiable evidence. However, as modern AI agents transition from static knowledge bases to continuous multimodal streams encompassing text, images, video, and audio, two critical challenges arise: maintaining index freshness without prohibitive re-indexing costs, and preserving cross-modal semantic consistency across heterogeneous embedding spaces. We present LUMA-RAG, a lifelong multimodal agent architecture featuring three key innovations: (i) a streaming, multi-tier memory system that dynamically spills embeddings from a hot HNSW tier to a compressed IVFPQ tier under strict memory budgets; (ii) a streaming CLAP->CLIP alignment bridge that maintains cross-modal consistency through incremental orthogonal Procrustes updates; and (iii) stability-aware retrieval telemetry providing Safe@k guarantees by jointly bounding alignment drift and quantization error. Experiments demonstrate robust text-to-image retrieval (Recall@10 = 0.94), graceful performance degradation under product quantization offloading, and provably stable audio-to-image rankings (Safe@1 = 1.0), establishing LUMA-RAG as a practical framework for production multimodal RAG systems.