Computational Overhead in Real-Time or Large-Scale Retrieval

Reduce the computational overhead of real-time and large-scale multimedia retrieval in question answering pipelines without degrading retrieval relevance or answer accuracy.

Background

Scaling multimedia retrieval to real-time or large corpora imposes significant computational costs due to high-dimensional embeddings, ANN search, and multimodal feature processing. The paper identifies this overhead as an unresolved challenge that constrains practical deployment and latency-sensitive applications, motivating research into efficient indexing, caching, model compression, and streaming-friendly architectures.

References

Despite recent progress, several challenges remain unresolved. Key issues include the difficulty of finegrained multimodal alignment (e.g., syncing spoken language with visual scenes), the lack of robust trustworthiness mechanisms such as modality attribution or segment-level citations, and the computational overhead introduced by real time or large scale retrieval. Further complexities arise in handling multilingual queries and supporting low-resource modalities, along with the persistent challenge of evaluating answer quality across modalities.

— Multimedia-Aware Question Answering: A Review of Retrieval and Cross-Modal Reasoning Architectures  (2510.20193 - Raja et al., 23 Oct 2025) in Conclusion (Section 5)

For very large datasets, the limited vocabulary of sign language may also reduce the need for global Top-$K$ retrieval. A fixed subset of the gallery may already provide useful retrieval candidates. However, this hypothesis still requires empirical validation.

— SignRAG: Unified Retrieval-Augmented Gloss-Free Sign Language Translation  (2610.11371 - Rao et al., 8 Oct 2026) in Appendix, Section 10.5, “Discussion on Million-Scale Retrieval”