arXiv:2604.14442v1 Announce Type: cross
Abstract: We present an empirical study of whether hierarchically structured, shared-weight recurrence can match the representational quality of independent-layer stacking in a Transformer-based language model. HRM-LM replaces L independent Transformer layers with a two-speed recurrent pair: a Fast module operating at every step for local refinement, and a Slow module operating every T steps for global compression. This recurrent hierarchy is unrolled for M = N x T steps with shared parameters. The central and most robust finding, supported by a parameter-matched Universal Transformer ablation (UniTF, 1.2B) across five independent runs, is a sharp empirical gap between the two approaches.
Behavior change beyond intervention: an activity-theoretical perspective on human-centered design of personal health technology
IntroductionModern personal technologies, such as smartphone apps with artificial intelligence (AI) capabilities, have a significant potential for helping people make necessary changes in their behavior