MADS: Multi-Agent Dialog Simulation for Robust Conversational Agents
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Computer Science > Computation and Language
arXiv:2610.12757 (cs)
[Submitted on 8 Oct 2026]
Title:MADS: Multi-Agent Dialog Simulation for Robust Conversational Agents
Authors:Arkajyoti Chakraborty, Adam Zernik, Ishika Kulkarni, Mudit Arora, Hugo Lin, Justin Chiu, Alessandro Di Bari, Neha Gupta, Andreas Stolcke
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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).
| Comments: | Submitted to Interspeech in February 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.12757 [cs.CL] |
| (or arXiv:2610.12757v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12757
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Arkajyoti Chakraborty [view email][v1] Thu, 8 Oct 2026 22:08:10 UTC (420 KB)
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View a PDF of the paper titled MADS: Multi-Agent Dialog Simulation for Robust Conversational Agents, by Arkajyoti Chakraborty and 8 other authors
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