ProactiveBench: Benchmarking Proactiveness in Multimodal Large Language Models
Effective collaboration begins with knowing when to ask for help. For example, when trying to identify an occluded object, a human would ask someone to remove the obstruction. Can MLLMs exhibit a similar "proactive" behavior by requesting simple user interventions? To investigate this, we introduce ProactiveBench, a benchmark built from seven repurposed datasets that tests proactiveness across different tasks such as recognizing occluded objects, enhancing image quality, and interpreting coarse sketches. We evaluate 22 MLLMs on ProactiveBench, showing that (i) they generally lack proactiveness; (ii) proactiveness does not correlate with model capacity; (iii) "hinting" at proactiveness yields only marginal gains. Surprisingly, we found that conversation histories and in-context learning introduce negative biases, hindering performance. Finally, we explore a simple fine-tuning strategy based on reinforcement learning: its results suggest that proactiveness can be learned, even generalizing to unseen scenarios. We publicly release ProactiveBench as a first step toward building proactive multimodal models.
69c0f6143fd3fef6237a693f
Thomas De Min
claimed_verified
2026-03-27T11:38:29.481Z
62f79e5a92950415b637d010
/avatars/f86cd0447c827eef9b8b25f248341455.svg
Thomas De Min
tdemin16
user
tdemin16
69c0f6143fd3fef6237a6940
Subhankar Roy
69c0f6143fd3fef6237a6941
Stéphane Lathuilière
69c0f6143fd3fef6237a6942
Elisa Ricci
69c0f6143fd3fef6237a6943
Massimiliano Mancini
ProactiveBench: Benchmarking Proactiveness in Multimodal Large Language Models
Effective collaboration begins with knowing when to ask for help. For example, when trying to identify an occluded object, a human would ask someone to remove the obstruction. Can MLLMs exhibit a similar "proactive" behavior by requesting simple user interventions? To investigate this, we introduce ProactiveBench, a benchmark built from seven repurposed datasets that tests proactiveness across different tasks such as recognizing occluded objects, enhancing image quality, and interpreting coarse sketches. We evaluate 22 MLLMs on ProactiveBench, showing that (i) they generally lack proactiveness; (ii) proactiveness does not correlate with model capacity; (iii) "hinting" at proactiveness yields only marginal gains. Surprisingly, we found that conversation histories and in-context learning introduce negative biases, hindering performance. Finally, we explore a simple fine-tuning strategy based on reinforcement learning: its results suggest that proactiveness can be learned, even generalizing to unseen scenarios. We publicly release ProactiveBench as a first step toward building proactive multimodal models.
MLLMs demonstrate limited proactive behavior in requesting user interventions for challenging tasks, with performance hindered by conversational context and in-context learning biases, though reinforcement learning fine-tuning shows potential for learning such behaviors.
MLLMs proactive behavior multimodal models reinforcement learning fine-tuning in-context learning conversation history benchmark