Is Reasoning Always Useful? Rethinking Reasoning Utility in Universal Multimodal Embeddings

Title:Is Reasoning Always Useful? Rethinking Reasoning Utility in Universal Multimodal Embeddings

Abstract:Reasoning-enhanced universal multimodal embeddings (UME) improve heterogeneous retrieval, but plausible rationales do not necessarily produce discriminative rankings. We study this gap by comparing the discriminative (DISC) and reasoning-driven generative (GEN) branches of UME-R1, a state-of-the-art reasoning UME method. We decompose reasoning utility into positive-target gain, hard-negative gain, and their margin difference. Positive similarity increases for 56.6%, but 15.7% are false-helpful cases where reasoning moves hard negatives closer even more. Local-neighborhood and token-attribution diagnostics suggest why: reasoning often de-condenses retrieved neighborhoods, but utility requires separator-aligned movement, while influential CoT tokens frequently encode evidence shared by positives and hard negatives. Motivated by these diagnostics, we propose SURE (Score-structure Utility Router for Embeddings), which improves UME-R1-7B by 1.5 points and yields consistent gains on two additional embedding models on MMEB-V2, without retraining, label-based policy selection, or extra VLM forward passes.

Artificial Intelligence (cs.AI)

arXiv:2609.29560 [cs.AI]

(or arXiv:2609.29560v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.29560 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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