Argues for modeling LLM interactions as dependent stochastic processes to enable valid sequential uncertainty quantification and change-point monitoring for trustworthiness properties.
Control Charts for Multi-agent Systems
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abstract
Generative agents have proven to be powerful assistants in a wide variety of contexts. Given this success, users are now deploying agents with minimal restrictions in open ended, multi-agent environments. Current methods for monitoring the dynamics of open-ended multi-agent systems are limited to qualitative inspection. In this paper, we extend the process-theoretic notion of adaptive control charts to multi-agent systems to enable automated monitoring. Using simulation, we demonstrate that adaptive control charts are necessary for monitoring multi-agent systems that can learn from their environment. We further demonstrate, both empirically and theoretically, that adaptive control charts are susceptible to adversarial agents that defect sufficiently slowly. These results illustrate a fundamental tradeoff in multi-agent system control: either agents in a system cannot learn or the system is susceptible to adversaries.
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Sequential statistical inference for Large Language Models: Representation, validity, and monitoring
Argues for modeling LLM interactions as dependent stochastic processes to enable valid sequential uncertainty quantification and change-point monitoring for trustworthiness properties.