AI RESEARCH
Implicit Statistical Inference in Transformers: Approximating Likelihood-Ratio Tests In-Context
arXiv CS.LG
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ArXi:2603.10573v1 Announce Type: new In-context learning (ICL) allows Transformers to adapt to novel tasks without weight updates, yet the underlying algorithms remain poorly understood. We adopt a statistical decision-theoretic perspective by investigating simple binary hypothesis testing, where the optimal policy is determined by the likelihood-ratio test. Notably, this setup provides a mathematically rigorous setting for mechanistic interpretability where the target algorithmic ground truth is known. By.