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Learning to Ask: Decision Transformers for Adaptive Quantitative Group Testing
Mahdi Soleymani, Tara Javidi
https://arxiv.org/abs/2509.01723 https://arxiv.…

Learning to Ask: Decision Transformers for Adaptive Quantitative Group Testing
We consider the problem of quantitative group testing (QGT), where the goal is to recover a sparse binary vector from aggregate subset-sum queries: each query selects a subset of indices and returns the sum of those entries. Information-theoretic results suggest that adaptivity could yield up to a twofold reduction in the total number of required queries, yet no algorithm has surpassed the non-adaptive bound, leaving its practical benefit an open question. In this paper, we reduce the QGT probl…