arXiv AI September 24, 2026 By Siddhi Patil, Navrati Saxena, William B. Andreopoulos

RelCheck: Dual-Evidence Spatial Grounding for VLM Hallucination Correction

RelCheck is a training‑free post‑hoc correction pipeline that addresses relational hallucinations in multimodal large language models. It augments object‑level visual grounding with two forms of relational evidence—learned scene‑graph triples from RelTR and deterministic spatial predicates derived from bounding‑box geometry—forming a three‑layer visual knowledge base. When applied to LLaVA v1 13B, RelCheck improves the overall MME hallucination score from 585.0 to 630.0, with the most significant gain on spatial position accuracy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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arXiv AI

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By Qinwu Xu

llms computer-vision reinforcement-learning multimodal benchmarks safety

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