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# Thompson Sampling

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researchsquare.com Oct 27, 2026
Modular XL-MIMO beam training stops sweeping the full codebook: parallax lays out candidates, statistics prune the noise-only sectors
Beam training for modular extremely large antenna arrays in hybrid near- and far-field environments no longer keeps a fixed candidate codebook. Prior subarray schemes relied on predefined codebooks or activation patterns and adapted poorly to spatially non-stationary channels; this scheme builds a parallax-aware initial codebook covering each subarray's angular shift, then learns online with a multi-armed bandit, and once a statistical steady state is detected it projects beams across subarrays and prunes noise-only sectors with a likelihood ratio test, compressing the action space. In simulation the scheme's effective spectral efficiency exceeds three comparison schemes across the evaluated SNR range, with markedly reduced training overhead.
Beam training for modular extremely large antenna arrays in hybrid near- and far-field environments no longer keeps a fixed candidate codebook. Prior subarray schemes relied on predefined codebooks or activation patterns and adapted poorly to spatially non-stationary channels; this scheme builds a parallax-aware initial codebook covering each subarray's angular shift, then learns online with a multi-armed bandit, and once a statistical steady state is detected it projects beams across subarrays and prunes noise-only sectors with a likelihood ratio test, compressing the action space. In simulation the scheme's effective spectral efficiency exceeds three comparison schemes across the evaluated SNR range, with markedly reduced training overhead.
Nanjing University of Posts and Telecommunications
Chuanpeng Xu（First author）Yunchao Song（Corresponding author）
Beam Training Extremely Large-Scale MIMO Multi-Armed Bandit Thompson Sampling Integer Programming
arXiv May 7, 2026
An ensemble-based information-theoretic task selection framework makes LLM multi-agent communication-structure optimization beat random training in both benign and adversarial settings
The work proposes an ensemble-based information-theoretic task selection framework that estimates task informativeness by how much a candidate task changes the distribution over graph parameters, using ensemble Kalman inversion as a derivative-free approximation of the Bayesian update, and combines embedding-based representative selection, surrogate modeling, and batch Thompson sampling for scalability; validated across benign and adversarial settings and multiple task formats, it consistently outperforms random training with more effective task selection and greater overall cost efficiency.
The work proposes an ensemble-based information-theoretic task selection framework that estimates task informativeness by how much a candidate task changes the distribution over graph parameters, using ensemble Kalman inversion as a derivative-free approximation of the Bayesian update, and combines embedding-based representative selection, surrogate modeling, and batch Thompson sampling for scalability; validated across benign and adversarial settings and multiple task formats, it consistently outperforms random training with more effective task selection and greater overall cost efficiency.
University of Wisconsin–Madison
Huchen Yang（First author）
Active Learning LLM-Based Multi-Agent Systems Communication Structure Optimization Ensemble Kalman Inversion Thompson Sampling

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