{"data":{"id":38,"backendId":"db0b6450-77fc-41fd-b290-3b50695c9f04","title":"Dense Supervision Is Not Enough: The Readout Blind Spot in Looped Language Models","summary":"arXiv:2606.24898v1 Announce Type: new Abstract: Looped language models turn hidden states into runtime state: each state is decoded for prediction and fed back into future computation. This creates a basic supervision question: which state variables does cross-entropy actually control? We show that dense per-loop cross-entropy controls the variables exposed by the readout, not every variable active in the recurrent transition. Hidden-state scale gives a concrete failure mode. Scale-invariant rea","analysis":"This paper identifies a specific, non-obvious failure mode in recurrent AI architectures. It provides actionable insights for model stability.","category":"technology","strategicTrack":"ai_agents","capitalRelevance":{},"tags":["looped language models","recurrent transformers","dense supervision","RMSNorm","hidden-state scale"],"qualityScore":10,"valueScore":8,"interestScore":8,"potentialScore":8,"uniquenessScore":9,"sourceCount":1,"confidence":5,"detectedAt":"2026-06-25T06:05:02.001Z","createdAt":"2026-07-03 08:06:36"}}