ProactiveBench: Benchmarking Proactiveness in Multimodal Large Language Models

E!ective collaboration begins with knowing when to ask forhelp. For example, when trying to identify an occluded object, a humanwould ask someone to remove the obstruction. Can MLLMs exhibit asimilar “proactive” behavior by requesting simple user interventions?To investigate this, we introduce ProactiveBench, a benchmark builtfrom seven repurposed datasets that tests proactiveness across di!erenttasks such as recognizing occluded objects, enhancing image quality, andinterpreting coarse sketches. We evaluate 22 MLLMs on ProactiveBench,showing that (i) they generally lack proactiveness; (ii) proactiveness doesnot correlate with model capacity; (iii) “hinting” at proactiveness yieldsonly marginal gains. Surprisingly, we found that conversation historiesand in-context learning introduce negative biases, hindering performance.Finally, we explore a simple fine-tuning strategy based on reinforcementlearning: its results suggest that proactiveness can be learned, evengeneralizing to unseen scenarios. We publicly release ProactiveBench asa first step toward building proactive multimodal models.