Architecture Determines Observability in Transformers

📰 ArXiv cs.AI

Learn how transformer architecture affects observability and error detection in autoregressive models

advanced Published 29 Apr 2026

Action Steps

Define observability in transformers as the linear readability of per-token decision quality from mid-layer activations
Control for max-softmax confidence and activation norm to isolate internal signals
Analyze how different architectures preserve internal signals and affect observability
Use activation monitoring to catch confident errors in autoregressive transformers
Apply corrections to improve model reliability and error detection

Who Needs to Know This

NLP engineers and researchers working with transformers can benefit from understanding the relationship between architecture and observability to improve model reliability

Key Insight

💡 Transformer architecture determines observability, which is crucial for catching confident errors in autoregressive models

Key Takeaways

Learn how transformer architecture affects observability and error detection in autoregressive models

Full Article

Title: Architecture Determines Observability in Transformers

Abstract:
arXiv:2604.24801v1 Announce Type: cross Abstract: Autoregressive transformers make confident errors, but activation monitoring can catch them only if the model preserves an internal signal that output confidence does not expose. This preservation is determined by architecture and training recipe. We define observability as the linear readability of per-token decision quality from frozen mid-layer activations after controlling for max-softmax confidence and activation norm. The correction is esse

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