arXiv:2606.07017v1 Announce Type: new Abstract: Foundation model agents are increasingly deployed for real-world decision-making, but suffer from the sim-to-real gap. While robotics and classical control have mature frameworks to address this gap, the foundation model community is treating agent robustness as an entirely novel phenomenon. Our paper proposes formalizing the foundation model agent evaluation and training gap as a classical sim-to-real problem structured entirely around the four elements of a Markov Decision Process, including Observation, Action, Transition, and Reward. In this
The increasing deployment of foundation model agents in real-world settings is exposing significant challenges that are now being formally addressed by researchers, drawing parallels to established engineering disciplines.
Formalizing the 'sim-to-real gap' for foundation model agents as a classical control problem provides a structured pathway for robust AI deployment, crucial for industries relying on autonomous systems.
The approach to evaluating and training foundation model agents shifts from ad-hoc robustness fixes to a principled engineering framework, integrating AI with established control theory.
- · AI developers
- · Robotics engineers
- · Industries deploying AI agents
- · AI safety researchers
- · Companies with naive AI deployment strategies
- · Unstructured AI evaluation methods
Improved reliability and safety of foundation model agents in real-world applications.
Accelerated deployment of autonomous AI systems in critical infrastructure and high-stakes environments.
The integration of AI engineering with traditional systems engineering becomes a standard practice, fostering new interdisciplinary fields.
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Read at arXiv cs.AI