The Agent's First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarios
arXiv:2601.08173v2 Announce Type: replace 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 \method{}, a dynamic evaluation environment that simulates a "trainee" agent continuously exploring a novel setting. Unlike tr
The rapid advancement of MLLMs necessitates more robust evaluation environments to bridge the gap between static lab results and dynamic real-world deployment challenges.
This development addresses a critical limitation in AI agent development, moving beyond ideal conditions to tackle real-world complexity, which is essential for generalizable and reliable autonomous systems.
The focus shifts from merely achieving high performance in controlled environments to building and benchmarking AI agents capable of continuous learning, exploration, and dynamic task scheduling in uncertain, stochastic settings.
- · AI agent developers
- · Workflow automation companies
- · Researchers in reinforcement learning
- · Industries deploying AI for complex tasks
- · Companies relying on static AI models
- · AI development methodologies ignoring real-world dynamics
Improved robustness and adaptability of AI agents in enterprise and operational settings.
Accelerated adoption of AI agents for complex, dynamic workflow automation across various sectors.
Potential for new business models built around highly adaptable and continuously learning autonomous systems.
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Read at arXiv cs.AI