Chapter 16

Introspection: White-Box Testing Networks

Use introspection as triage evidence, not proof of correctness. Compare internal signals across known-good, known-bad, ambiguous, and new-version examples. Treat attention, activations, probes, and sparse autoencoder features as measurement systems that need validation. Use…

Apply this chapter

Use introspection as triage evidence, not proof of correctness.
Compare internal signals across known-good, known-bad, ambiguous, and new-version examples.
Treat attention, activations, probes, and sparse autoencoder features as measurement systems that need validation.
Use drift in internal signals to focus behavioral evals where the model likely changed.

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12 focused briefs

Concepts in this chapter

123
Inputs and TokenizationWhite-box testing should begin with the evidence the model actually received, not with an exciting interpretation of its neurons.
124
Input Token TablesBefore builders inspect attention or activations, they need to see exactly what the model received as tokens.
125
Attention DiagnosticsAttention views can direct an investigation, but they are not transcripts of reasoning and they do not prove causality.
126
Final Prompt-Position Attention Across Decoder LayersThe final prompt position is where a decoder-only model prepares its first next-token prediction.
127
Attention Received by Each Input Token by LayerAttention received by token and layer helps Confidence Engineers notice whether constraints, negations, citations, or safety terms were ignored.
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Layer-by-Layer Attention MatricesAttention matrices expose token-to-token routing patterns that can be compared across layers, prompts, and model versions.
129
Neural Architecture as a Test SurfaceA transformer is a chain of internal transformations, and each transformation can become a source of observability evidence.
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Activation and Concept ProbesActivation and feature probes are emerging comparison tools, not validated meters for meaning, safety, or correctness.
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Concept Signal ProfilesResidual, attention, and MLP signals answer different testing questions about what the model carries, attends to, and transforms.
132
Concept MLP NeuronsCandidate concept neurons can be useful probes for ideas like privacy, security, uncertainty, or hallucination, but they are not magic meaning…
133
Sparse Autoencoders for AI TestingSparse autoencoders can separate messy raw activations into more interpretable features that may become useful monitoring signals.
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Future Network-Aware FrameworksFuture confidence systems may combine behavioral and internal evidence, but the internal measurement system will need testing too.