CORE: Improving Compositional Reasoning in MLLM Embedding via Reranker Distillation

Paper recorded by Signals 4 on 2026-09-03 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-03 on arXiv · recorded by Signals 4 on 2026-09-04

Category: cs.CL · 自然语言处理 · first seen 2026-09-04

Abstract

MLLM-based embedding models remain limited in compositional retrieval, often failing to distinguish scenes containing the same concepts but different attribute-object bindings. Yet the same backbone can resolve such distinctions when used as a cross-attentive reranker, motivating us to distill its compositional judgments into the embedding model. We propose CORE, which synthesizes candidate lists

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