arXiv Computation and Language
Oct 5

CUEing User Simulators: Calibrated User Embeddings for Multi-Turn Benchmarking

The paper introduces Calibrated User Embeddings (CUE), a framework that encodes real user sessions into continuous representations and decodes them into persona commands to steer large language models (LLMs) as user simulators without additional training. CUE enables user-conditioned replay of past interactions and generates novel personas that better align with real-user success rates and failure patterns. Evaluations on the τ²‑Bench dataset show that CUE‑driven simulators commit fewer simulator‑attributed errors, more accurately reproduce real‑user failure modes, and maintain competitive user fidelity across various tasks and LLMs.

By Anjali Kantharuban, Jonas Mueller
arXiv Computation and Language
6d ago

Towards User Simulators That Stay On Task And Still Sound Natural

The paper introduces UserLM‑2.0, a reinforcement‑learning based user simulator that balances task intent adherence with conversational naturalness. It achieves this by jointly rewarding both objectives and training a lightweight discriminator in an adversarial loop to measure naturalness. The resulting model generates valid simulated conversations 46.2% of the time, more than triple the success rate of the best prior simulator.

By Mustafa Omer Gul, Philippe Laban, Tanya Goyal, Jennifer Neville
arXiv Computation and Language
Oct 8

ToolRACER: A Robust Agentic Conversation Emulation Resource for Agent Training and Evaluation

arXiv:2610.09163v1 Announce Type: new Abstract: Task-oriented conversational agents remain fragile under real world conversation scenarios as they rarely follow a predictable script, especially when...

By Arkajyoti Chakraborty, Aryan Tayal, Ishika Agarwal, Tanner Sorensen, Justin Chiu, Alessandro Di Bari, Neha Gupta, Andreas Stolcke