Llm agents improve understanding despite user miscommunication and changing goals
Beyond Oracle Communication: Benchmarking Interactive Intent Alignment Under Miscommunication and Evolving User Intent
Computation and LanguageArtificial IntelligenceMachine Learning
Summary
Sometimes when people talk to AI helpers, they don’t explain what they really want clearly, or they change their minds along the way. The authors study how AI agents can better understand and keep up with these changing and unclear instructions. They made a new set of tests called Drift-Bench++ to see how well agents handle these tricky conversations. Their results show that while better conversations help AI understand users, there is still a big gap compared to perfect understanding. This research helps make AI assistants better at handling real-world chat where people don’t always explain themselves perfectly.
What this means in practice
- •For conversational ai developers: Evaluate and improve chatbots’ ability to handle users changing their goals or explaining poorly during conversations.
- •For customer support automation teams: Test and refine automated agents to maintain effective communication despite unclear or evolving customer requests.
Authors
Zheyuan Zhang, Mengyuan Chao, Ke Xiao, Ziyi Chen, Daoan Zhang, Yan Zhang, Yanfang Ye, Wei Xu
Abstract
Modern LLM agents increasingly tackle complex tasks through interactive, long-horizon exchanges with users, while existing benchmarks generally assume that users always accurately and sufficiently communicate a fixed intent. However, this oracle communication assumption rarely holds in practice: users may miscommunicate, change their goals, and run out of patience. We define this task setting as Interactive Intent Alignment, where agents must recover and continuously track the user's current intent despite imperfect communication and evolving goals. To study this setting, we introduce Drift-Bench++, a principled benchmark construction pipeline for verified executable tasks with controlled misalignment and intent shifts, along with an interaction protocol featuring finite patience, diverse simulated users, and silent interaction-conditioned shifts. We further develop GRIP, a comprehensive evaluation protocol covering task grounding, user realism, inquiry effectiveness, and adaptation to evolving intent. Across diverse environments, models, and interaction conditions, stronger interaction consistently helps but remains far from oracle performance; Validation on deployed ProdAgent sessions further shows that the modeled failures are prevalent and consequential in deployment. By providing a unified, executable benchmark for interactive intent alignment, Drift-Bench++ offers a foundation for evaluating and advancing agents under realistic communication and evolving intent.