Title: CrossCheck: A Vision-Language Conflict Detection Benchmark
Primary area: datasets and benchmarks
Keywords: conflict detection, multimodal llms, benchmark dataset

Abstract: Contradictory multimodal inputs are common in real-world settings, yet existing benchmarks typically assume input consistency and fail to evaluate cross-modal conflict detection -- a fundamental capability for preventing hallucinations and ensuring reliability.
We introduce CrossCheck, a novel benchmark for multimodal conflict detection, featuring COCO images paired with contradictory captions containing controlled object--level or attribute--level conflicts. Each sample includes targeted questions evaluated in both multiple-choice and open-ended formats. The benchmark provides an extensive fine-tuning set filtered through automated quality checks, alongside a smaller human-verified diagnostic set. Our analysis of state-of-the-art models reveals substantial limitations in recognizing cross-modal contradictions, exposing systematic modality biases and category-specific weaknesses. Furthermore, we empirically demonstrate that 
targeted fine-tuning on CrossCheck substantially enhances conflict detection capabilities.

TL;DR: We present CrossCheck, a benchmark for multimodal conflict detection using images paired with controlled contradictory captions.