![[2602.14229] CORPGEN: Simulating Corporate Environments with Autonomous Digital Employees in Multi-Horizon Task Environments](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.14229] CORPGEN: Simulating Corporate Environments with Autonomous Digital Employees in Multi-Horizon Task Environments
Summary
The paper introduces CORPGEN, a framework for simulating corporate environments using autonomous digital employees, addressing long-horizon task management challenges.
Why It Matters
As organizations increasingly rely on AI for complex task management, understanding how to effectively simulate and manage multi-horizon tasks is crucial. CORPGEN provides insights into improving AI performance in real-world corporate settings, highlighting architectural innovations that enhance task execution and efficiency.
Key Takeaways
- CORPGEN addresses long-horizon reasoning challenges in AI by simulating corporate environments.
- The framework improves task management efficiency, achieving up to 3.5x better performance than existing methods.
- Identifies critical failure modes in task execution, such as context saturation and memory interference.
- Utilizes hierarchical planning and adaptive summarization to enhance multi-horizon goal alignment.
- Experiential learning is shown to significantly boost performance in complex task environments.
Computer Science > Artificial Intelligence arXiv:2602.14229 (cs) [Submitted on 15 Feb 2026] Title:CORPGEN: Simulating Corporate Environments with Autonomous Digital Employees in Multi-Horizon Task Environments Authors:Abubakarr Jaye, Nigel Boachie Kumankumah, Chidera Biringa, Anjel Shaileshbhai Patel, Sulaiman Vesal, Dayquan Julienne, Charlotte Siska, Manuel Raúl Meléndez Luján, Anthony Twum-Barimah, Mauricio Velazco, Tianwei Chen View a PDF of the paper titled CORPGEN: Simulating Corporate Environments with Autonomous Digital Employees in Multi-Horizon Task Environments, by Abubakarr Jaye and 10 other authors View PDF HTML (experimental) Abstract:Long-horizon reasoning is a key challenge for autonomous agents, yet existing benchmarks evaluate agents on single tasks in isolation. Real organizational work requires managing many concurrent long-horizon tasks with interleaving, dependencies, and reprioritization. We introduce Multi-Horizon Task Environments (MHTEs): a distinct problem class requiring coherent execution across dozens of interleaved tasks (45+, 500-1500+ steps) within persistent execution contexts spanning hours. We identify four failure modes that cause baseline CUAs to degrade from 16.7% to 8.7% completion as load scales 25% to 100%, a pattern consistent across three independent implementations. These failure modes are context saturation (O(N) vs O(1) growth), memory interference, dependency complexity (DAGs vs. chains), and reprioritization overhead. We pres...
