Seeing is not sharing: VL models overestimate common ground in asymmetric dialogue

In the field of conversational artificial intelligence, one of the most subtle yet critical challenges is the difference between what a system can observe and what has actually been agreed upon during an interaction. A recent study on vision-language models (VLMs) reveals a concerning trend: these models tend to overestimate common ground in asymmetric dialogues, assuming that visible or available information is equivalent to shared knowledge, when in reality mutual understanding must be built step by step through linguistic negotiation. This phenomenon has direct implications for the design of virtual assistants, remote support systems, and collaborative platforms where the correct interpretation of spatial or contextual references is vital. At Q2BSTUDIO we understand that true human-machine collaboration is not achieved simply by equipping the system with sensory capabilities, but by developing mechanisms that track the evolution of the dialogue and distinguish between potential information and actually established knowledge. That is why we offer artificial intelligence services for businesses that integrate advanced models with dynamic grounding techniques, avoiding over-alignment biases and improving accuracy in complex tasks. Our custom applications incorporate AI agents capable of managing multimodal dialogues, from customer service platforms to interactive training tools. Furthermore, we combine these solutions with AWS and Azure cloud services to scale securely, along with robust cybersecurity that protects sensitive data exchanged in every conversation. Business intelligence, powered by Power BI, allows monitoring system performance and continuously adjusting interpretation models. Ultimately, the key to truly collaborative AI lies in building systems that not only see, but understand what has actually been shared—a principle that guides each of our developments.