aiqbench analysis
This research introduces Monte Carlo Query Search (MCQS), a novel active learning framework designed to rigorously evaluate the generalizability of black-box AI agents in unseen environments. By synthesizing queries that distinguish between pessimistic and optimistic capability models, the method efficiently maps agent boundaries with fewer interactions than baseline strategies. The work provides theoretical guarantees on soundness and convergence, offering a critical tool for safely deploying foundation model agents where outcome predictability is paramount.
Autonomous Assessment of Generalizability of AI Agent Capabilities
arXiv:2512.16733v4 Announce Type: replace Abstract: Safe deployment of black-box AI (BBAI) systems such as foundation model agents requires methods for evaluating their capabilities in novel settings. We define an agent's capability as its ability to achieve a short term objective and formalize the problem of learning models that predict whether, with what effects, and under what conditions, an agent can perform a capability. We introduce Monte Carlo Query Search (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 evaluation as an active learning problem over policies. We use Monte Carlo tree search to synthesize queries that maximally distinguish between extremal capability hypotheses: the lattice meet and join corresponding to the most pessimistic and optimistic models consistent with observed behavior. Executing these queries yields trajectories that prune inconsistent hypotheses. We prove soundness, completeness, and convergence properties under standard realizability and sampling assumptions. Experiments with multiple BBAI systems show that MCQS learns accurate capability models more efficiently than baseline query strategies, enabling systematic characterization of agent capability boundaries with fewer interactions.
Abstract:Safe deployment of black-box AI (BBAI) systems such as foundation model agents requires methods for evaluating their capabilities in novel settings. We define an agent's capability as its ability to achieve a short term objective and formalize the problem of learning models that predict whether, with what effects, and under what conditions, an agent can perform a capability. We introduce Monte Carlo Query Search (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 evaluation as an active learning problem over policies. We use Monte Carlo tree search to synthesize queries that maximally distinguish between extremal capability hypotheses: the lattice meet and join corresponding to the most pessimistic and optimistic models consistent with observed behavior. Executing these queries yields trajectories that prune inconsistent hypotheses. We prove soundness, completeness, and convergence properties under standard realizability and sampling assumptions. Experiments with multiple BBAI systems show that MCQS learns accurate capability models more efficiently than baseline query strategies, enabling systematic characterization of agent capability boundaries with fewer interactions.
Artificial Intelligence (cs.AI)
arXiv:2512.16733 [cs.AI]
(or arXiv:2512.16733v4 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2512.16733 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 272 words
Related coverage
Expenditure Horizon: Measuring Optimization Ability, with an Application to NanoGPT
METR · Jul 21, 2026
GLM-5.3 Sometimes Considers How Train-Time Actions Shape Future Behavior
LessWrong · Oct 15, 2026
Import AI 470: No rights for machines; automating environment generation with SPADE; and building better GPU kernels with Hawkeye
Import AI · Aug 24, 2026
What's Continual Learning, and Why Might We Expect To See It In Advanced LLM Agents?
LessWrong · Jun 12, 2026