DIVERT: Efficient LLM Agent Evaluation via Diversity-Guided Simulation
A new framework called DIVERT (Diversity-Induced Evaluation via Branching of Trajectories) aims to improve the efficiency and thoroughness of evaluating large language model (LLM) agents in customer-facing roles. Current evaluation methods rely on linear Monte Carlo rollouts of complete agent-user conversations, which are computationally wasteful as they repeatedly regenerate identical early conversation prefixes and often miss rare user behaviors that lead to deep failures. DIVERT captures the full agent-environment state at critical decision points, allowing execution to resume from these snapshots. This enables reuse of shared conversation prefixes, reducing redundant computation. From each junction, the framework branches to explore diverse user behaviors, systematically covering rare scenarios. The approach is described as efficient, snapshot-based, and coverage-guided, designed for systematic exploration of agent-user interactions. The research is published on arXiv under identifier 2604.21480.
Key facts
- DIVERT stands for Diversity-Induced Evaluation via Branching of Trajectories.
- It is a framework for evaluating LLM agents in multi-turn interactions.
- Current evaluation uses linear Monte Carlo rollouts, which are inefficient.
- DIVERT captures agent-environment state at critical decision points.
- It resumes execution from snapshots to reuse shared conversation prefixes.
- The framework branches from each junction to explore rare user behaviors.
- The goal is to uncover deep failure modes from rare user behaviors.
- The research is published on arXiv (2604.21480).
Entities
Institutions
- arXiv