@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://aclanthology.org/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."
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%0 Conference Proceedings
%T The Agent’s First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarios
%A Fu, Daocheng
%A Mei, Jianbiao
%A Wu, Rong
%A Yang, Xuemeng
%A Xu, Jia
%A Wang, Ding
%A Cai, Pinlong
%A Liu, Yong
%A Wen, Licheng
%A Shi, Botian
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F fu-etal-2026-agents
%X 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.
%U https://aclanthology.org/2026.findings-acl.1505/
%P 30094-30109
Markdown (Informal)
[The Agent’s First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarios](https://aclanthology.org/2026.findings-acl.1505/) (Fu et al., Findings 2026)
ACL
- Daocheng Fu, Jianbiao Mei, Rong Wu, Xuemeng Yang, Jia Xu, Ding Wang, Pinlong Cai, Yong Liu, Licheng Wen, and Botian Shi. 2026. The Agent’s First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarios. In Findings of the Association for Computational Linguistics: ACL 2026, pages 30094–30109, San Diego, California, United States. Association for Computational Linguistics.