arXiv:2602.06014v2 Announce Type: replace-cross Abstract: Thompson sampling (TS) is widely used for stochastic multi-armed bandits, yet its inferential properties under adaptive data collection are subtle. Classical asymptotic theory for sample means can fail because arm-specific sample sizes are random and coupled with the rewards through the action-selection rule. We study adaptive inference for Thompson sampling with Gaussian randomized indices in $K$-armed stochastic bandits with independent sub-Gaussian reward noises, and identify \emph{optimism} as a key mechanism for restoring \emph{sta
This research is emerging as AI systems are increasingly deployed in adaptive decision-making contexts, highlighting the need for robust theoretical guarantees for their performance.
Improving the inferential properties of Thompson Sampling can lead to more reliable and trustworthy AI agents, especially for critical applications where adaptive data collection is inherent.
The identified 'optimism' mechanism provides a theoretical foundation for understanding and enhancing the stability of adaptive inference in widely used reinforcement learning algorithms.
- · AI researchers and developers
- · Sectors deploying adaptive AI (e.g., healthcare, finance)
- · AI agents
- · Systems relying on less stable adaptive inference methods
Adaptive AI algorithms like Thompson Sampling gain improved theoretical understanding and practical reliability.
This improved reliability fosters greater adoption of AI agents in complex, real-world decision-making scenarios.
Enhanced trust in adaptive AI may accelerate the development and integration of fully autonomous AI systems into critical infrastructure.
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Read at arXiv cs.AI