Query-Conditioned Spherical Centroid Aggregation for Multimodal Retrieval

• URL: http://arxiv.org/abs/2609.15335v1
• Date: Mon, 14 Sep 2026 10:21:23 GMT
• Title: Query-Conditioned Spherical Centroid Aggregation for Multimodal Retrieval
• Abstract summary: Multimodal retrieval integrates video, audio, subtitles, and text.<n>Recent geometric aggregators treat all modalities symmetrically.<n>SCALAR is a query-conditioned aggregator that assigns relevance-based weights to each available modality.
• Score: 5.4018351446007875
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal retrieval integrates video, audio, subtitles, and text; however, recent geometric aggregators, such as Gramian volumes, hyperbolic volumes, and spectral objectives, treat all modalities symmetrically. Under a unified evaluation protocol, their joint scores frequently lag behind the strongest single-modality pathway by 1.9 to 27.6 R@1. Controlled analyses attribute this outcome to uniform modality influence. This work introduces Spherical Centroid Aggregation with Learned Adaptive Relevance (SCALAR), a query-conditioned aggregator that assigns relevance-based weights to each available modality before computing a spherical centroid. SCALAR accommodates arbitrary modality subsets and is trained on masked, reduced-arity views using rank-8 LoRA adapters. Across five benchmarks, SCALAR achieves positive aggregation gain on four, reaching +4.0 R@1, while none of the evaluated prior aggregators is positive on more than one. A uniform-weight ablation reproduces the degradation observed with symmetric aggregation. With only 4.8 million trainable parameters, SCALAR attains the highest text-to-video R@1 on three and performs within seed variation of the best result on a fourth. Under test-time modality dropout, SCALAR's representation-stage score surpasses the released GRAM checkpoint at every evaluated masking rate and benchmark by 3.2 to 10.9 R@1. Finally, as modalities are removed, rerankers trained exclusively on complete modality sets increasingly converge toward their video-only pathways, diminishing these representation-level gains and underscoring a limitation of standard two-stage retrieval pipelines.

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