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Attention-based rerankers score documents by aggregating query-to-document attention and subtracting a null-query calibration pass to remove positional and structural bias. Although widely used, this calibration assumes that the null pass removes irrelevant signal from each document. We show that modern prompt content,...
To disentangle visual evidence at both semantic and spatial levels, ProtoLIP is introduced, a lightweight prototype-mediated evidence layer that organizes reusable visual prototypes into text-derived semantic families and uses coarse-to-fine evidence routing, where semantic families constrain prototype eligibility and...
This paper proposes KeySI, an interaction framework that enables feature-level feedback through keyword-based concept specification that reduces the need for manual document inspection and labeling and lowers the barrier to adapting embedding models.
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