SIGNPOST-Bench: Evaluating Text-Vision Conflict Resolution in MLLMs
Sirun Li · hf · 2026-08-07
Researchers introduced SIGNPOST-Bench, a benchmark designed to evaluate how Multimodal Large Language Models (MLLMs) arbitrate between conflicting visual and textual cues.
- Methodology: The study transforms source images into a counterfactual quintuplet (Original, Blank, Similar, Random, Adversarial) using synthetic, localized scene-text interventions.
- Scale & Evaluation: The benchmark contains 5,111 counterfactual groups and 25,555 image variants from four datasets. It evaluates 20 MLLMs from seven providers.
- Key Findings: Adversarial variants raise the median localization error from 282 km to 1,347 km, a 4.8-fold increase. Among geocodable adversarial samples, 6.5% to 20.1% of predictions fall within 50 km of the injected target across models.
- Conclusion: Clean-input localization performance does not fully predict robustness to conflicting text, providing a controlled framework for evaluating multimodal evidence arbitration.
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