MINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active Elicitation

Researchers specializing in artificial intelligence published a new study on the arXiv preprint server on February 10, 2025, introducing the Minimal Information Neuro-Symbolic Tree (MINT) framework to improve how AI agents address knowledge gaps during collaborative tasks with humans. The team developed this system to solve the recurring problem of incomplete information in open-world planning, where AI systems often struggle because they lack specific details regarding physical objects or the underlying intentions of their human partners. By leveraging a neuro-symbolic approach, the researchers aim to create more efficient interaction strategies that allow software agents to proactively ask the right questions and elicit necessary information to complete complex objectives. The core of the MINT framework addresses the complexities of 'human-AI teaming,' a field where natural language interaction is essential but often hampered by ambiguity. In real-world scenarios, an AI might be tasked with a goal but may find itself missing critical data points, such as the location of an item or the specific preferences of a user. Traditional models often fail in these 'knowledge-gap' scenarios, leading to paralysis or incorrect actions. MINT utilizes a tree-based structure that balances the need for information gathering with the efficiency of planning, ensuring that the AI only asks for the 'minimal' amount of human input required to resolve uncertainty and proceed with the task. Furthermore, this research contributes significantly to the development of 'Active Elicitation' in object-driven planning. Instead of passively waiting for commands, the MINT system empowers agents to recognize what they do not know and strategically engage the human user to fill those gaps. This development marks a transition from reactive AI to proactive collaborators that can navigate the unknowns of the open world. By integrating symbolic reasoning with neural networks, the researchers have provided a scalable roadmap for creating autonomous systems that are more intuitive, communicative, and effective in joint problem-solving environments.