arXiv:2607. 17060v1 Announce Type: new Abstract: A Bayes-filtered transformer (BFT) is a transformer trained on sequences that are generated in two steps: first a latent task is drawn from a prior, then observations are drawn conditional on that task.
By Afiq Abdillah Effiezal Aswadi, Haotong Ma, Susan Wei
arXiv:2507. 11768v3 Announce Type: replace-cross Abstract: Bayesian accounts of in-context learning face a direct objection: exact posterior predictives for exchangeable data are invariant to task-preserving order, yet transformers change next-token probabilities when the same examples are serialized differently.
By Leon Chlon, Fatima Sheaib, Zein Khamis, Maggie Chlon, Mahdi El Zein, MarcAntonio M. Awada
arXiv:2606. 31184v1 Announce Type: cross Abstract: Adaptive experiments for average treatment effects (ATE) require randomized allocations balancing valid inference with statistical efficiency.
By Jiachun Li, David Simchi-Levi
arXiv:2507. 01414v2 Announce Type: replace Abstract: We introduce a new family of toy problems that combine features of linear-regression-style continuous in-context learning (ICL) with discrete associative recall.
By Sultan Daniels, Dylan Davis, Dhruv Gautam, Wentinn Liao, Gireeja Ranade, Anant Sahai
arXiv:2602. 04596v2 Announce Type: replace-cross Abstract: Bayes-filtered transformers are transformers meta-learned on sequences from a prior predictive distribution to approximate the corresponding posterior predictive distribution.
By Sandra Fortini, Kenyon Ng, Sonia Petrone, Judith Rousseau, Susan Wei
arXiv:2512. 24780v2 Announce Type: replace Abstract: Neural networks trained with standard objectives exhibit behaviors characteristic of probabilistic inference: soft clustering, prototype specialization, and Bayesian uncertainty tracking.
By Alan Oursland