arXiv AI By Prabal Vashisht, Zheng Hu, Ying Yang

Reproducible Evaluation as the Substrate for Safe Prompt Compression in Production Agents

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The paper introduces an offline evaluation harness that remains reproducible even as live enterprise data changes, allowing for on-demand regeneration when benchmarks become stale. This harness is used as a fitness function for GEPA-based prompt compression, with a correctness gate to prevent reward hacking. In a production merchant-facing agent, the method reduces the prompt size by 3.5‑7.6% while maintaining or improving task correctness across multiple model families.

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arXiv Machine Learning
Oct 28

Intent-Preserving Adversarial User Simulation for Multi-Turn Agent Evaluation

The paper introduces StressSim, an intent‑preserving adversarial user simulator designed to stress‑test multi‑turn agents by generating challenging yet valid user interactions. StressSim optimises high‑level adversarial plans for each task instance using a genetic prompt‑optimisation loop that leverages rollout traces, task‑completion signals, and validity feedback to reduce task success while keeping the user’s goal unchanged. Experiments on MultiWOZ, retail, and airline domains show that StressSim reveals significant robustness gaps in leading LLM agents—reducing tau‑bench pass^4 scores by up to 69%—while maintaining realistic conversation quality and goal preservation with a modest optimisation budget.

By Atri Vivek Sharma, Brian Formento, Alessio Lomuscio
arXiv AI
Jul 15

Operationalising Multi-Dimensional Evaluation for Conversational Agents: A Scalable, Governed Pipeline with Selective Re-evaluation and Model Benchmarking

arXiv:2607. 12085v1 Announce Type: new Abstract: Evaluating retail conversational agents requires methods beyond lexical-overlap metrics to assess intent alignment, factuality, helpfulness, clarity, tone, and overall response quality.

By Niranjan Kumar M, Balaji Nagarajan, Karthik Nair, Faysal Satter, Nithin Surendran