Paper recorded by Signals 4 on 2026-10-06 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-06 on arXiv · recorded by Signals 4 on 2026-10-07
Category: cs.LG · 机器学习 · first seen 2026-10-07
Endpoint accuracy on abstract-reasoning benchmarks does not reveal whether a language model has acquired a transferable rule or fit distribution-specific regularities. We study this distinction in small language models on the ARC-TGI benchmark, which organizes abstract grid transformations into controllable task families and supports resampling, spatial shifts, and cross-benchmark transfer. Across