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arXiv · 2507.15501

ASPERA: A Simulated Environment to Evaluate Planning for Complex Action Execution

Alexandru Coca·Mark Gaynor·Zhenxing Zhang·Jianpeng Cheng·Bo-Hsiang Tseng·Pete Boothroyd·Héctor Martinez Alonso·Diarmuid Ó Séaghdha·Anders Johannsen
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Abstract

This work evaluates the potential of large language models (LLMs) to power digital assistants capable of complex action execution. These assistants rely on pre-trained programming knowledge to execute multi-step goals by composing objects and functions defined in assistant libraries into action execution programs. To achieve this, we develop ASPERA, a framework comprising an assistant library simulation and a human-assisted LLM data generation engine. Our engine allows developers to guide LLM generation of high-quality tasks consisting of complex user queries, simulation state and corresponding validation programs, tackling data availability and evaluation robustness challenges. Alongside the framework we release Asper-Bench, an evaluation dataset of 250 challenging tasks generated using ASPERA, which we use to show that program generation grounded in custom assistant libraries is a significant challenge to LLMs compared to dependency-free code generation.

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Alexandru Coca, Mark Gaynor, Zhenxing Zhang, Jianpeng Cheng, Bo-Hsiang Tseng, Pete Boothroyd, Héctor Martinez Alonso, Diarmuid Ó Séaghdha, Anders Johannsen. 2025-07-21. ASPERA: A Simulated Environment to Evaluate Planning for Complex Action Execution. https://arxiv.org/abs/2507.15501

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