274 citations · 649 across the 21 of their papers we have counts for
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cs.CV2026
Loud and Clear: Making Visual Evidence Count in Multimodal Document RAG
Eunchan Jo, Seounghyun Jang, Sangwoo Mo
Multimodal document retrieval-augmented generation (RAG) has focused largely on retrieval, yet its answers depend on how well a vision-language model reads the retrieved pages. We…
cs.AI2026
Why, Where, How: Taxonomy-guided Error Grounding for Code Repair in NL2SQL
Suchan Lee, Woomin Song, Hwanjo Yu +1
SQL queries that large language models write from natural language questions can execute successfully yet produce incorrect results, so execution alone does not reveal what to fix.…
cs.CV2026
Stay Fair! Ensuring Group Fairness in Diffusion Models Across Guidance Scales
Myeongsoo Kim, Eunji Kim, Minwoo Chae +1
Diffusion models steer conditional generation with a tunable guidance scale to trade off prompt alignment and diversity. However, existing debiasing techniques are optimized for a…