Section 127 · Chapter 16, Introspection: White-Box Testing Networks
Attention Received by Each Input Token by Layer
Attention received by token and layer helps Confidence Engineers notice whether constraints, negations, citations, or safety terms were ignored.
confidence engineerattentionattention received by each layer
What to do
- Report regressions by slice, not only by average attention.
- Define runnable checks that exercise confidence engineer, attention, and attention received by each layer.
- Set acceptable outcomes and blocker failures for confidence engineer, attention, and attention received by each layer before running the evaluation.
Evidence to preserve
- Report regressions by slice, not only by average attention.
- Preserve the inputs, versions, configurations, raw outcomes, and results for confidence engineer, attention, attention received by each layer needed to reproduce work on Attention Received by Each Input Token by Layer.
- Report results for confidence engineer, attention, attention received by each layer by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
The deeper move is to measure attention received by categories of tokens: constraints, negations, tool outputs, citations, user identity, dates, amounts, and safety policy terms. Report regressions by slice, not only by average attention.
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Cite this page
Jason Arbon. "Attention Received by Each Input Token by Layer." Testing AI Knowledge Edition, section 127.
https://jarbon.ai/testing-ai/knowledge/ch127-attention-received-by-each-layer.html