arXiv AI
3d ago

Discarded or Unreachable? Diagnosing and Repairing Spatial Relation Failure in Vision--Language Similarity

The paper investigates why contrastive vision‑language models often fail to capture left/right spatial relations, identifying two distinct failure modes: an access failure where the relation exists in the pooled embedding but is unreachable by native similarity, and a pooling failure where the relation is lost during pooling but retained in dense features. The authors propose a zero‑parameter, query‑conditioned spatial moment that recomputes geometry from the patch grid and augments the native embedding, dramatically improving left/right pairwise accuracy and recall on large image indexes while preserving native rankings for non‑relational queries.

By Mahdi Mostajabdaveh, Zirui Zhou
arXiv AI
Oct 16

Loud and Clear: Making Visual Evidence Count in Multimodal Document RAG

The paper introduces Loud and Clear (LoC), a method that enhances multimodal document retrieval-augmented generation by fully utilizing visual token capacity and refining retrieved pages to highlight evidence. LoC combines higher-resolution pages with OCR of query-relevant regions, achieving significant accuracy gains over eight prior methods on multiple benchmarks. The study demonstrates that the way retrieved pages are presented to the generator is a critical bottleneck beyond retrieval itself.

By Eunchan Jo, Seounghyun Jang, Sangwoo Mo
arXiv Computation and Language
Aug 31

CamoDocs: A Poisoning Attack Against Retrieval-Augmented Language Models Using Camouflaged Documents

arXiv:2608. 28389v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) augments LLMs with external documents, but public or user-editable sources expose RAG systems to data poisoning: attackers can inject malicious documents to steer outputs toward targeted answers.

By Jaewon Jung, Haizhong Zheng, Hongsun Jang, Jaeyong Song, Beidi Chen, Jinho Lee