arxiv
PublishedJuly 21, 2026 at 4:00 AM
Mobius Learning: Cyclic Depth Folding in Transformers
Publisher summary· verbatim
arXiv:2607.17843v1 Announce Type: cross Abstract: Transformer-based language models organize computation along an ordered depth axis, where shallow and deep blocks often develop distinct representational roles. We challenge the conventional view that these roles must remain tied to a block's positio
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivCapacity and Redundancy Trade-offs in Multi-Task Learning6harxivPredictive Training with Latent Imagination for Visual Quadruped Navigation6harxivWhere Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making6harxivDid We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection6hThe Bubble Brief
WEEKLYRead AI insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗