EGAL-SC: State-Conditioned Evidence Admission for Multimodal Retrieval Agents

Retrieval controls can recover missing evidence while excluding support needed to answer the same question. Effective retrieval therefore requires deciding which candidates should enter the answering context. We introduce EGAL-SC, a multimodal retrieval framework that pairs controlled and unrestricted searches with a state-conditioned evidence admission rule. The rule combines relevance, alignment with accumulated evidence, novelty, and a learned document-error signal. On 562 held-out first-control states from MultiModalQA and WebQA, threshold-free ranking of the same candidate pools under a common five-document cap yields 272 complete-evidence outcomes, compared with 239 for maximal marginal relevance, and retains 49 additional support units. The complete-evidence difference passes a four-test Holm correction; the smaller difference from removing the error signal does not. In a seven-system comparison on two 500-question panels, EGAL-SC obtains 21.933% all-question F1 versus 19.520% for Agentic Hybrid, with broader answer coverage and higher computation. On jointly answered questions, its F1 is lower. These results support state-conditioned evidence ranking and show that system-level answer gains must be assessed together with answer selection and cost.