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AI models would rather guess than ask for help, researchers find

THE DECODER / 4/11/2026

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Key Points

  • Researchers introduced ProactiveBench to evaluate whether multimodal language models seek help when visual context is missing.
  • Testing 22 multimodal models showed that almost none ask users for clarification, instead proceeding with guesses or fabricated assumptions.
  • The study suggests a reinforcement learning–based approach can improve the tendency to ask for help when the model lacks required information.
  • The findings highlight a reliability and safety gap in current multimodal systems: they may fail gracefully by guessing rather than proactively communicating uncertainty.

ProactiveBench tests whether multimodal language models ask users for help when visual information is missing. Out of 22 models tested, almost none ask for what they need, but a simple reinforcement learning approach hints at a fix.

The article AI models would rather guess than ask for help, researchers find appeared first on The Decoder.

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