Multi-Object Hallucination in Vision-Language Models

Large vision language models (LVLMs) often suffer from object hallucination, producing objects not present in the given images. While current benchmarks for object hallucination primarily concentrate on the presence of a single object class rather than individual entities, this work systematically investigates multi-object hallucination, examining how models misperceive (e.g., invent nonexistent objects or become distracted) when tasked with focusing on multiple objects simultaneously. We introduce Recognition-based Object Probing Evaluation (ROPE), an automated evaluation protocol that consid

Research goal: How does the predictive expert caching strategy in ExpertFlow affect object existence hallucination rates (e.g., on POPE) compared to dense models of equivalent FLOPs in multimodal MoE-VLMs?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.8/10.