DeepMind's Recirculation Evolves Transformers Without Retraining

Google DeepMind and UT Austin propose Recirculation, a training-free method that recirculates activations to help Transformers maintain evolving internal states, addressing state loss in long-context processing.

2026-08-23 ~ 2026-08-25 · 3 related posts

• DeepMind's Recirculation Enables Recursive Self-Improvement Without Retraining — omarsar0 · 2026-08-23
• Google DeepMind Introduces Recirculation to Improve Transformer State Maintenance — burkov · 2026-08-25
• DeepMind Paper: Leaking Activations Back Fixes Long-Context State Loss — rohanpaul_ai · 2026-08-25