diff --git a/experiments/command_specialist/EVIDENCE_DIAGNOSTICS.md b/experiments/command_specialist/EVIDENCE_DIAGNOSTICS.md
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+# Nonempty negative evidence diagnostics
+
+Two fresh development comparisons on September 10, 2026 tested runtime context
+changes with the unchanged `shell-specialist-pilot` model and adapter. Neither
+candidate was adopted. This is evidence for the next English-worker iteration,
+not a new inspection interface or an instruction to retrain.
+
+Each comparison used 12 saved synthetic cases, three paired runs, alternating
+source/treatment blocks and full-workload warmups. Real public CLI calls included
+planning, native file execution, evidence selection and raw-artifact reopening.
+Source snapshots and SHA-256 input guards passed. Context/output stayed 4096/160.
+Model responses, errors and raw output were retained. Historical corpora and the
+frozen final-test partition were not opened.
+
+| Runtime treatment | Baseline correct | Treatment correct | New regressions |
+| --- | ---: | ---: | ---: |
+| Add an explicit instruction to return an empty list for no matches | 15/36 | 15/36 | 0 |
+| Add three small negative/positive selection examples | 15/36 | 21/36 | 3 |
+
+Correctness required the actual executed stdout, expected source-line set and
+verbatim evidence. Cases included routine-only pages, a no-errors message,
+standalone error/summary lines, present/absent requested identifiers, and a
+100-line page with the error/summary at 99/100. These deliberately challenging
+cases are not interchangeable with earlier five-operation planning benchmarks.
+
+The examples fixed two singleton negatives and a singleton error, but replaced
+the correctly selected long-page error at line 99 with unrelated line 79 in every
+run. They also selected all lines of a routine-only page, omitted a standalone
+summary, and returned an unrelated identifier for an absent requested identifier.
+The aggregate score conceals a consequential regression; do not adopt it.
+
+The one-sentence instruction did not repair any task. It reduced generation on
+one failure path, but no evidence-quality improvement was established. Neither
+comparison establishes production reliability, cold performance, throughput gains
+or whole-agent speed. Invalid selections and truncations remain visible failures;
+valid line numbers alone do not establish relevance or completion.
+
+For the English execution loop, retain task-specific observable completion checks
+and raw results. Distinguish empty stdout from a nonempty page with no relevant
+lines. Do not silently treat a selected line or an exit-zero process as proof that
+the requested work was done. Keep this diagnostic separate from the other
+session's loop implementation and configured-host verification.
+
+All 288 warmup/measured records remain locally under the mission worktree's
+`work/evidence-abstention/` and `work/evidence-context/`, including the runnable
+`evaluate.py`, source snapshots, fresh fixtures, manifests and per-case artifacts.
+`work/EVIDENCE_DIAGNOSTICS.md` contains detailed per-case scores and timing figures.
+Those artifacts and experimental prompt variants are intentionally outside Git.
diff --git a/experiments/command_specialist/README.md b/experiments/command_specialist/README.md
index 8fc9cc1..a9946bf 100644
--- a/experiments/command_specialist/README.md
+++ b/experiments/command_specialist/README.md
@@ -230,6 +230,9 @@ just exact command text. Evidence success requires the exact relevant line set;
 recall and precision are also reported. Invalid JSON, invalid plans, timeouts, and
 invalid line selections count as failures. The runtime guarantees verbatim selected
 text, but cannot guarantee that the model selected all relevant evidence.
+See the [nonempty negative-case diagnostics](EVIDENCE_DIAGNOSTICS.md): a small
+context treatment raised aggregate accuracy while regressing long-page evidence,
+so it was not adopted.
 
 `rules` is a real non-ML evidence-selection baseline. `deterministic` is an oracle
 typed-input latency floor; it must not be presented as natural-language accuracy.