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This paper presents a hybrid search framework that combines Thompson sampling with parallel self-avoiding walks to adaptively allocate computational effort across restriction classes for the LABS problem. The method improves previously best-known merit factors for 35 sequence lengths and achieves a new longest sequence with merit factor exceeding 8.0.