ARGUS: An Agenda-Driven Framework for Reliable User Simulation

Abstract:High-fidelity user simulators are essential for evaluating and training agents in interactive environments. However, current LLM-based simulators struggle with reliability issues, such as goal omission and policy non-compliance. We propose ARGUS (Agenda-dRiven Goal-oriented User Simulator), a neuro-symbolic framework that maintains the user's goal state using a dynamic agenda stack manipulated via symbolic operations. By rigorously decoupling state tracking from natural language generation, ARGUS facilitates strict goal progression and constraint-aligned interactions. For cost-efficient deployment, we distill ARGUS into compact models using a unified pipeline: evaluator-guided SFT followed by online RL with dense step-level rewards. To rigorously evaluate behavioral fidelity, we introduce a rubric-based metric Composite Behavioral Score (CBS). Evaluations on $\tau^3$-Bench and MultiWOZ demonstrate the significant superiority of the ARGUS architecture and the efficacy of our training pipeline. Code and data will be released soon.

Artificial Intelligence (cs.AI)

arXiv:2610.21346 [cs.AI]

(or arXiv:2610.21346v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.21346 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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