Architecture Determines Observability of Transformers
Autoregressive transformers make confident errors that output-confidence monitoring cannot catch. Activation monitors catch them only when training leaves a decision-quality signal beyond what the output already exposes. This signal is an architectural property of the trained model, fixed upstream of any monitor. Controlling for output confidence removes 60.3% of the raw activation-probe signal on average across 14 models. Raw probe signal is mostly output confidence, and output-side readouts cannot recover the residual. What remains depends on architecture and training. In Pythia's controlled training, both matched-width configurations form the signal early. One preserves it through conv...
| Authors | Thomas Carmichael |
|---|---|
| Primary category | cs.LG |
| Published | 27 April 2026 |
| Last updated at source | 12 May 2026 |
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