arXiv:2512.16733v3 Announce Type: replace Abstract: Black-box AI (BBAI) systems, including foundation-model agents, are increasingly used for sequential decision making. Safe deployment requires methods for characterizing what such systems can do, when they can do it, and what outcomes may result. We introduce Monte Carlo Query Synthesis (MCQS), an active query-synthesis method for learning symbolic stochastic capability models of BBAIs. MCQS models capabilities as conditional probability distributions over outcomes and formulates capability learning as an active learning problem over policies
The increasing deployment of black-box AI systems necessitates robust methods for understanding their capabilities and ensuring safe, predictable operation, which MCQS directly addresses.
This development is crucial for safely deploying advanced AI, particularly foundation models and agents, by providing a systematic way to assess their performance and limitations in a dynamic environment.
The ability to actively synthesize queries to map out and model the probabilistic capabilities of AI systems introduces a new paradigm for AI evaluation and safety.
- · AI Safety Researchers
- · AI Development Platforms
- · Organizations deploying AI
- · Unquantifiable AI Systems
- · AI Development without explainability focus
Improved predictability and reliability of AI agents in complex decision-making scenarios increase their adoption curve.
Standardized capability models could lead to new regulatory frameworks and certification processes for AI systems.
Enhanced trust in AI agents could accelerate their integration into critical infrastructure and sensitive applications, including national security.
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Read at arXiv cs.AI