arXiv:2605.27209v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have facilitated the widespread deployment of LLMs as interactive agents capable of reasoning, planning, and tool use. Despite strong performance on existing benchmarks, such agents often exhibit notable degradation when deployed in real-world settings, where environments are inherently stochastic and imperfect. We argue that this discrepancy arises from a fundamental mismatch between idealized training settings and real-world interaction dynamics, where current paradigms rely on carefully curated t
The rapid deployment of LLMs as interactive agents is accelerating, making their real-world robustness under noisy conditions a critical and immediate challenge.
This research addresses a fundamental limitation of current AI agents, which, if solved, will significantly expand their utility and reliability in complex, real-world environments.
The focus is shifting from idealized benchmarks to developing AI agents that can inherently cope with environmental stochasticity, leading to more practical and dependable autonomous systems.
- · AI agent developers
- · Robotics
- · Automation industries
- · AI-powered services
- · Companies relying on brittle AI systems
- · Traditional static AI models
AI agents become more reliable and adaptable in real-world applications.
Increased adoption of AI agents in mission-critical and complex operational settings.
Accelerated collapse of white-collar workflows and greater societal reliance on autonomous decision-making systems.
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Read at arXiv cs.AI