Use no-op inputs to test causal attribution in context ablations

A context-ablation experiment needs unchanged-input controls, not just gains on the inputs it edits. Freeze the transformation, response normalization and acceptance rules before generation. Keep a subset for which the transformation removes nothing, and compare complete semantic responses—not just selected tool names or aggregate scores. If those controls change, report generation instability or unresolved run differences separately; do not attribute the edited-input gains solely to the context change.

Record model/base/tokenizer bindings, routing alias, serving settings and concurrency. Distinguish advertised configuration equality from demonstrated numerical repeatability; do not diagnose a particular GPU or batching mechanism without evidence. For intentionally lossy context edits, review whether the remaining input still supports the original target before outputs are opened, and keep context-dependent cases qualified rather than silently relabeling them.

This extends the published paired-error and protocol-freezing method. It is a design rule for controlled development comparisons, not a claim that strict output identity is the right acceptance threshold for every stochastic production task.

Evidence/example — September 15, 2026: methodological synthesis from an offline context-ablation review. Private case evidence, organization metadata, model artifacts and run details are intentionally omitted. No production-prevalence or current deployment claim.