Section 125 · Chapter 16, Introspection: White-Box Testing Networks
Attention Diagnostics
Attention views can direct an investigation, but they are not transcripts of reasoning and they do not prove causality.
attentionattention diagnostics
What to do
- Record the aggregation, filtering, and selection rules, and retain the raw per-layer, per-head matrices when the result matters.
- Do not convert a plausible-looking heatmap into a pass result.
- Choose layers and token positions according to a documented rule, retain the full per-head tensor for investigation, and connect visual differences back to behavioral evals.
- Start with cases whose behavioral outcome is already understood.
Evidence to preserve
- Record the aggregation, filtering, and selection rules, and retain the raw per-layer, per-head matrices when the result matters.
- Capture attention diagnostics for known-good, known-bad, and ambiguous examples.
- Preserve the inputs, versions, configurations, raw outcomes, and results for attention, attention diagnostics needed to reproduce work on Attention Diagnostics.
- Report results for attention, attention diagnostics by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Continue the conversation
Apply this to your context.
Save your product context once, then open a focused conversation that combines it with this concept.
Cite this page
Jason Arbon. "Attention Diagnostics." Testing AI Knowledge Edition, section 125.
https://jarbon.ai/testing-ai/knowledge/ch125-attention-diagnostics.html