ABBEL

BAIR introduces ABBEL: teaching LLMs to update beliefs for efficient long-horizon interaction

Researchers from UC Berkeley's BAIR Lab present ABBEL, a method for training LLMs to perform principled belief updating during long-horizon interactive tasks, reducing the number of clarifying questions needed while maintaining task accuracy. The approach targets a core inefficiency in current LLM-based agents: failure to maintain and revise a coherent model of user intent across multi-turn interactions. The work is positioned as a step toward more efficient human-AI collaboration in agentic settings.