Prism: Towards Lowering User Cognitive Load in LLMs via Complex Intent Understanding

• URL: http://arxiv.org/abs/2601.08653v1
• Date: Tue, 13 Jan 2026 15:30:48 GMT
• Title: Prism: Towards Lowering User Cognitive Load in LLMs via Complex Intent Understanding
• Authors: Zenghua Liao, Jinzhi Liao, Xiang Zhao,
• Abstract summary: Large Language Models are rapidly emerging as web-native interfaces to social platforms.<n>Existing approaches attempt to clarify user intents through sequential or parallel questioning.<n>Inspired by the Cognitive Load Theory, we propose Prism, a novel framework for complex intent understanding.
• Score: 8.863937298785347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models are rapidly emerging as web-native interfaces to social platforms. On the social web, users frequently have ambiguous and dynamic goals, making complex intent understanding-rather than single-turn execution-the cornerstone of effective human-LLM collaboration. Existing approaches attempt to clarify user intents through sequential or parallel questioning, yet they fall short of addressing the core challenge: modeling the logical dependencies among clarification questions. Inspired by the Cognitive Load Theory, we propose Prism, a novel framework for complex intent understanding that enables logically coherent and efficient intent clarification. Prism comprises four tailored modules: a complex intent decomposition module, which decomposes user intents into smaller, well-structured elements and identifies logical dependencies among them; a logical clarification generation module, which organizes clarification questions based on these dependencies to ensure coherent, low-friction interactions; an intent-aware reward module, which evaluates the quality of clarification trajectories via an intent-aware reward function and leverages Monte Carlo Sample to simulate user-LLM interactions for large-scale,high-quality training data generation; and a self-evolved intent tuning module, which iteratively refines the LLM's logical clarification capability through data-driven feedback and optimization. Prism consistently outperforms existing approaches across clarification interactions, intent execution, and cognitive load benchmarks. It achieves stateof-the-art logical consistency, reduces logical conflicts to 11.5%, increases user satisfaction by 14.4%, and decreases task completion time by 34.8%. All data and code are released.

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