Research questionHow can multimodal LLM agents satisfy must-have constraints, rank preferences, and abstain when no solution exists?Agents may misread which user requirements are mandatory, return options that violate them, or fail to recognize infeasibility. When several feasible options remain, they must use softer preferences without treating them as hard constraints. Latest papersRecent research connected to this question, newest first.First Things First: Teaching LLM-Based Agents to Prioritize Must-Haves before Nice-to-HavesThe source evaluates state-of-the-art multimodal LLMs on 3,649 constructed problems covering unique feasible solutions, preference-based selection among multiple solutions, and cases with no valid solution. The scenarios represent e-commerce, booking, map-based, and ride-hailing services; reported evidence also includes results on LogicVista, MathVision, and InfoQA. The source proposes a reinforcement-learning approach, but the evidence is benchmark-based rather than a deployment study.research paper · Sep 4, 2026