Just Keep Prompting (JKP): New Framework Tests Vision-Language Model Stability Under Repeated Questioning

Researchers from ArXiv cs.CL introduced Just Keep Prompting (JKP), a new multi-turn evaluation framework that measures how well Vision-Language Models (VLMs) maintain epistemic stability when users repeatedly challenge, question, or contradict their answers. VLMs are AI systems that understand both images and text, such as describing a photo and then reassessing that description under sustained conversational pressure.

JKP probes models for up to 10 follow-up turns using three distinct strategies: Adversarial Negation (repeatedly rejecting the model's answer), Pure Socratic Interrogation (repeatedly calling on the model to reassess its certainty), and Context-Aware questioning (providing additional context that may contradict the model's initial response).

This research matters because deploying VLMs in real-world settings requires not only strong visual reasoning but also stability under sustained conversational pressure. For example, if an AI is asked to analyze a medical scan and a clinician repeatedly questions its findings, JKP helps ensure the AI doesn't become confused or inconsistent. This could make AI more trustworthy in critical areas like healthcare, education, and customer service.

If you're curious about how this works, you can explore the full research paper on ArXiv. While you can't directly test JKP yourself, understanding this framework helps you see why AI needs to be robust under pressure.