@inproceedings{fu-etal-2026-agents,
    title = "The Agent{'}s First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarios",
    author = "Fu, Daocheng  and
      Mei, Jianbiao  and
      Wu, Rong  and
      Yang, Xuemeng  and
      Xu, Jia  and
      Wang, Ding  and
      Cai, Pinlong  and
      Liu, Yong  and
      Wen, Licheng  and
      Shi, Botian",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1505/",
    pages = "30094--30109",
    ISBN = "979-8-89176-395-1",
    abstract = "The rapid evolution of Multi-modal Large Language Models (MLLMs) has advanced workflow automation; however, existing research mainly targets performance upper bounds in static environments, overlooking robustness for stochastic real-world deployment. We identify three key challenges: dynamic task scheduling, active exploration under uncertainty, and continuous learning from experience. To bridge this gap, we introduce TraineeBench, a dynamic evaluation environment that simulates a ``trainee'' agent continuously exploring a novel setting. Unlike traditional benchmarks, TraineeBench evaluates agents along three dimensions: (1) context-aware scheduling for streaming tasks with varying priorities; (2) prudent information acquisition to reduce hallucination via active exploration; and (3) continuous evolution by distilling generalized strategies from rule-based, dynamically generated tasks. Experiments show that cutting-edge agents have significant deficiencies in dynamic environments, especially in active exploration and continual learning. Our work establishes a framework for assessing agent reliability, shifting evaluation from static tests to realistic, production-oriented scenarios."
}