Subtraction Gets You More: Gap-Aware Retrieval for Multimodal Multi-Hop QA

• URL: http://arxiv.org/abs/2605.28641v1
• Date: Wed, 27 May 2026 15:46:21 GMT
• Title: Subtraction Gets You More: Gap-Aware Retrieval for Multimodal Multi-Hop QA
• Abstract summary: We propose a paradigm that performs implicit query rewriting directly at the embedding level.<n>By context-subtractive query steering, GRAIL excels at compositional cross-modal reasoning.<n>By dynamically routing queries based on task type, our Hybrid Framework achieves a 40.3% macro-averaged performance gain on MultimodalQA.
• Score: 11.316299961548415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In multimodal multi-hop question answering, we focus on the initial retrieval stage via two distinct tasks: (1) evidence set completion, retrieving missing evidence given context, and (2) sequential pool construction, iteratively building the top-$K$ pool from the scratch. Under these settings, we point out that conventional iterative retrieval frameworks often suffer from Semantic Anchoring, where previously fetched evidence traps the retriever and yields entity-centric redundancy. To break this trap, we propose GRAIL (Gap-aware Retrieval via Adaptive Implicit Localization), a paradigm that performs implicit query rewriting directly at the embedding level. By context-subtractive query steering, GRAIL excels at compositional cross-modal reasoning, while additive embedding updates show strength on localized information aggregation. By dynamically routing queries based on task type, our Hybrid Framework achieves a 40.3\% macro-averaged performance gain on MultimodalQA. Extensive evaluations demonstrate that sequential GRAIL retrieves in a superior, noise-resilient manner, significantly expanding the search horizon through iterative gap-aware optimization.

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