MADS: Multi-Agent Dialog Simulation for Robust Conversational Agents

Abstract:Task-oriented conversational agents remain fragile under realistic conditions, especially when users exhibit non-cooperative behavior. Existing function-calling benchmarks underrepresent adversarial conversation trajectories, thereby limiting the training resources available for developing robust agents. We present MADS (Multi-Agent Dialog Simulation), a synthetic data generation pipeline that produces structured, diverse dialogue trajectories. We inject adversarial conversation, producing validated conversation trajectories that capture realistic, unhappy paths. Using MADS, we construct MADS-Bench: a benchmark spanning six domains, 55 personas, and 1095 conversations, with approximately 66% of conversations containing failure-prone conversation scenarios. We evaluate the robustness of the trained models using a two-stage training approach that combines supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR).

Computation and Language (cs.CL)

arXiv:2610.12757 [cs.CL]

(or arXiv:2610.12757v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.12757 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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