What You Can't See Is Still What You Learn: A Preregistered Sixty-Society Confirmation That Evidence Masking Drives Compositional Generalization

The study investigates whether restricting a module’s access to information—through evidence masking—enhances a system’s ability to learn compositional tasks. In a preregistered experiment with sixty‑four‑cell systems built on a frozen language‑model backbone, researchers varied evidence masking, ownership markers, and filler replacement across multiple initialization clusters and data orders. Results showed that when markers were available, masking significantly improved accuracy on held‑out two‑ and three‑operation compositions, with all tested pairs meeting performance thresholds and the preregistered behavioral criterion satisfied. The study also explored packet interventions and found predicted intermediate‑value changes, though mediation was not conclusively established. "whyItMatters":"The findings demonstrate a substantial performance benefit from evidence masking in compositional generalization tasks, offering a promising direction for designing more effective learning systems."

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.