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This paper identifies sufficient sample sizes for maximum-likelihood support recovery of sparse binary signals in high-SNR regimes, revealing an information-theoretic threshold and trade-offs between measurement sparsity and computational cost.
STRIDE is a new framework for training data attribution in LLMs that models functional effects in activation space using sparse recovery and steering operators, achieving state-of-the-art accuracy with 13x speedup over previous methods.