LUMA-RAG: Lifelong Multimodal Agents with Provably Stable Streaming Alignment

TL;DR

LUMA-RAG tackles the challenge of lifelong, streaming multimodal grounding by integrating a two-tier memory system (hot HNSW and warm IVFPQ), a streaming CLAP→CLIP alignment bridge updated via orthogonal Procrustes, and stability-informed retrieval telemetry. The approach achieves provable bounds on retrieval perturbations (Safe@k) while maintaining low latency and graceful degradation under product-quantization offloading; experiments show high Recall@10 and Safe@1 guarantees in audio-to-image and text-to-image tasks. Key contributions include the dynamic memory spill policy, the streaming bridge with incremental alignment updates, and a formal stability framework that bounds ranking drift under streaming updates. This yields a practical, production-ready framework for continuous multimodal RAG that preserves cross-modal coherence without frequent re-indexing, enabling robust deployments in dynamic environments.

Abstract

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.

Figures (5)

• Figure 1: LUMA-RAG system architecture: hot HNSW tier for low-latency recall, warm IVFPQ tier for capacity, streaming CLAP$\rightarrow$CLIP alignment bridge, and stability-aware retrieval.
• Figure 2: Streaming process flow: Data Sources $\rightarrow$ Multimodal Encoders $\rightarrow$ Alignment $\rightarrow$ Vector Index $\rightarrow$ Retrieval & Ranking $\rightarrow$ Answerer/UI.
• Figure 3: Baseline text$\rightarrow$image retrieval.
• Figure 4: Retrieval under memory offloading (group-aware).
• Figure 5: Audio$\rightarrow$image retrieval with a streaming bridge.

Theorems & Definitions (7)

• Lemma 1: Cosine Lipschitzness
• proof
• Theorem 1: Top-$1$ Stability
• proof
• Theorem 2: Top-$k$ Set Stability
• proof
• Lemma 2: Bridge Drift Bound