Measuring Cross-Task Behavioral Consistency in Language Model Agents
The episode discusses a paper introducing the Behavioral Consistency Metric (BCM) to evaluate language model agents by measuring their stability across different tasks, moving beyond simple success rates. Hosts detail how BCM uses twelve-dimensional structural features and attribution vectors to quantify cross-task consistency, highlighting that agents can be locally consistent but globally fragmented. They conclude with practical suggestions like drift detection and consistency-augmented reinforcement learning for building more reliable AI.