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Monitorability is an architectural property before it is a monitor-design problem. We define observability: the linear readability of per-token decision quality from frozen mid-layer activations after controlling for max-softmax confidence and activation norm. Confidence controls absorb on average 60.3% of raw probe signal across 14 models in 6 families.\n  Observability is not a generic property of transformers. In Pythia's controlled suite, all three tested runs at the 24-layer, 16-head configuration collapse to rho_partial ~0.10 across a 3.5x parameter gap and two Pile variants, while six other configurations occupy a separated healthy band from 0.21 to 0.38. The output-controlled residual r_OC collapses at the same points; neither nonlinear prob","title":"Architectural Observability Collapse in Transformers","url":"https://arxiv.org/abs/2604.24801","vendor":"arxiv_cs_ai"},"summary":"arXiv:2604.24801v2 Announce Type: replace-cross \nAbstract: Activation monitoring can catch confident errors in autoregressive transformers only if training preserved an internal decision-quality signal that output confidence does not expose. Monitorability is an architectural property before it is a monitor-design problem. We define observability: the linear readability of per-token decision quality from frozen mid-layer activations after controlling for max-softmax confidence and activation norm. Confidence controls absorb on average 60.3% of raw probe signal across 14 models in 6 families.\n  Observability is not a generic property of transformers. In Pythia's controlled suite, all three tested runs at the 24-layer, 16-head configuration collapse to rho_partial ~0.10 across a 3.5x parameter gap and two Pile variants, while six other configurations occupy a separated healthy band from 0.21 to 0.38. 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