An eval that exits with 0 while the run metadata says verification_passed=false is not a harmless inconsistency. It is a fork in the truth system. One branch says the patch is safe enough to ship. The other says the patch did not prove the thing it was supposed to prove. If the disputed fix is a double-fire bug, the cost is concrete: duplicated jobs, duplicated customer messages, duplicated charges, duplicated side effects, and incident reviews that start from the wrong premise because the gate already recorded a pass.
The failure mode is common in autonomous engineering loops. A repair experiment executes a command like pytest, records exit_code=0, and then asks a verifier to decide whether the fix is proven. The verifier returns false because it cannot find evidence that the target behavior was exercised. Both answers can be technically honest. The command passed. The repair was not verified. The system is broken because the pass bit and the proof bit have been allowed to describe different questions.
For a double-fire fix, this distinction matters more than usual. A normal functional bug can often be caught by broad regression coverage. Double-fire bugs are temporal, stateful, and edge-triggered. They appear when two schedulers race, when a retry and a success callback both publish, when a debounce key is scoped too loosely, or when an idempotency guard is checked after the side effect instead of before it. A generic test suite can go green forever while the exact interleaving that caused duplicate execution remains untested.
The right interpretation is therefore severe: exit 0 is only transport success. It proves the eval command completed without a test failure. It does not prove the eval is relevant. The sprint should not start by tuning the model, rewriting the whole harness, or arguing over metadata naming. It should start by forcing the eval to answer one narrow question: when the historical double-fire trigger is replayed, does exactly one side effect happen, and does the verification layer bind that observation to verification_passed=true only when the target assertion is present?
The fastest way to waste five days is to fix the test runner before naming the behavior. A double-fire bug needs a precise event contract. Not a vague statement like should not run twice, but an event-level invariant that can be observed from outside the implementation. The invariant should have four parts: trigger, identity, side effect, and window.
The trigger is the smallest reproducible stimulus that previously caused duplication. Examples: one webhook delivery plus one retry, one scheduler tick overlapping another, one queued job rehydrated after a timeout, or one UI submit followed by a network retry. The identity is the business key that should collapse those attempts into one operation: payment_id, job_id, campaign_id, message_id, or a composite like account_id + date + task_kind. The side effect is the irreversible action: insert a row, send an email, publish a post, charge a card, enqueue a downstream job, or write a completion marker. The window is the interval in which the duplicate can occur, usually short enough to simulate but long enough to cover retry jitter and scheduler overlap.
A useful target statement looks like this: given two invocations of dispatch(task_id="A") inside the same lease window, exactly one side_effect_committed event may be emitted for task A, and every loser path must record duplicate_suppressed before returning success. That sentence is testable. It does not depend on a model's narrative. It does not care whether the implementation uses a lock, an idempotency table, a compare-and-swap update, or a lease token. It cares about externally visible behavior.
Once the target is defined, every eval artifact should carry it. The eval name should not be test_regression. It should be something like test_dispatch_suppresses_double_fire_for_same_task_id. The run metadata should include target_bug="double_fire", target_invariant="single_side_effect_per_identity", and required_assertions=["side_effect_count_equals_one", "duplicate_path_observed"]. This is not bureaucratic decoration. It prevents a broad unrelated test command from masquerading as verification.
pytest tests/ -q exits 0, but the report contains no assertion about side-effect count.The core design error is usually a single boolean doing two jobs. exit_code answers whether the command failed. verification_passed must answer whether the target behavior was proven. When those fields disagree, the harness should preserve the disagreement, not average it away. The sprint should make that split explicit in code and in operator-facing output.
A clean contract is: command_passed = exit_code == 0; evidence_present = required_assertions subset_of observed_assertions; verification_passed = command_passed and evidence_present and no_forbidden_events. This makes the false-green state understandable. If command_passed=true and evidence_present=false, the eval ran successfully but did not test the fix. If command_passed=false, the test command itself failed. If no_forbidden_events=false, the target bug reproduced or another guardrail was violated.
The implementation can be small. The evaluator should emit a structured record instead of relying on human interpretation of logs. A minimal record has fields like eval_id, target_bug, command, exit_code, observed_assertions, forbidden_events, artifact_paths, and verification_passed. The verifier should reject any run where target_bug is missing or where observed_assertions does not include the required invariant.
The key is that observed_assertions cannot be model-written prose. It has to be derived from executable checks. A test can write ASSERTION side_effect_count_equals_one to a JSON artifact only after it counted the sink. A replay harness can write ASSERTION duplicate_path_observed only after it saw the losing invocation take the suppression branch. The verifier then reads machine evidence, not a summary paragraph.
This also solves the unrelated-tests question. If the command points at a broad suite but the evidence artifact lacks side_effect_count_equals_one, the answer is no: the eval did not verify the double-fire fix. It merely ran tests that did not fail. If the command points at the target replay and emits the required assertions, the answer is yes. The ambiguity disappears because relevance is no longer inferred from the command name or the surrounding narrative.
Most double-fire tests are flaky because they try to discover a race at runtime. That is backwards. The eval should not depend on the operating system scheduler being unlucky. It should make the double invocation deterministic by controlling the boundary where the original race occurred.
Suppose the production path is claim_task(), perform_side_effect(), mark_complete(). If the historical bug happened because two workers both passed claim_task(), the test should not spawn random threads and hope. It should use a barrier or fake store that allows two calls to observe the same pre-claim state, then releases both. If the fix uses an atomic insert into idempotency_keys, the replay should prove one insert wins and one loses. If the fix uses a lease row, the replay should prove only one valid lease token can reach the side-effect function.
The deterministic fixture needs three observability points. First, count calls to the attempted entry point: attempt_count == 2. Without this, the test might pass because it only fired once. Second, count committed side effects: side_effect_count == 1. Without this, the test does not protect the real cost. Third, count suppression or loser-path events: suppressed_count == 1. Without this, the second call may have crashed, hung, or silently skipped a branch that still fails in production.
For synchronous code, a fake sink is enough. Replace the outbound effect with RecordingSink.commit(identity), call the handler twice with the same identity, and assert the sink contains one committed record. For asynchronous code, the fixture should use controlled queues and explicit drains: enqueue two identical messages, release both workers at the claim boundary, wait for the queue to settle, then inspect the sink and the event log. For scheduler code, freeze time and run two ticks with the same due item. For webhook code, deliver the same payload twice with the same signature and event ID.
The fixture should also contain a negative control. Run the same two invocations with different identities and assert two side effects occur. That prevents a blunt global lock from passing the test while destroying throughput. A correct idempotency fix suppresses duplicates for the same identity; it does not serialize unrelated work into one-at-a-time sludge. The negative control makes the eval harder to game and more useful as a regression guard.
The verification gate should be hostile to accidental success. A passing unrelated suite is useful as background regression signal, but it should not be able to flip the fix-specific proof bit. The simplest rule is that every fix gate has a manifest, and the manifest names the exact evidence it requires.
A manifest for this case could declare bug_id="double_fire_dispatch", required_tests=["test_same_identity_double_fire_suppressed", "test_distinct_identities_not_suppressed"], required_artifacts=["double_fire_replay.json"], and required_assertions=["attempt_count_equals_two", "side_effect_count_equals_one", "suppressed_count_equals_one", "negative_control_commits_two"]. The verifier reads the manifest first. Then it reads the run output. If the test command exits 0 but the artifact is absent, verification_passed=false is correct. If the artifact exists but does not include the required assertion IDs, false is still correct. If the assertion IDs exist but the raw counts contradict them, the artifact is invalid.
This is where many harnesses are too polite. They treat missing artifacts as unknown and then allow an upstream pass to dominate. That is how an unrelated test suite becomes a fake fix. The gate should instead mark the run as not_verified with a reason like missing_required_artifact or required_assertion_absent. The command can remain green while the fix gate remains red. That is not a contradiction. It is the point.
The manifest should live near the fix, not hidden inside a central evaluator nobody reads. A practical layout is evals/double_fire_dispatch/manifest.json, evals/double_fire_dispatch/replay_test.py, and evals/double_fire_dispatch/verify.py. The application tests can stay where they are. The important part is that the repair experiment references the manifest by path and that the verifier refuses to infer relevance from the existence of a green test run.
There is one more guard: mutation. Temporarily remove or invert the idempotency check and confirm the target eval fails. This is not theater. A test that still passes after the fix is deliberately broken is not testing the fix. During the sprint, run the replay once with the guard disabled or with a feature flag forcing the old behavior. The expected result is exit_code != 0 or verification_passed=false with side_effect_count=2. If the eval cannot fail under the broken implementation, it cannot prove the repaired one.
Day one is for evidence inventory and target naming. Collect the failing run where exit 0 and verification_passed=false disagree. Identify the exact command, the artifacts it produced, and the reason the verifier refused to pass. Then write the invariant in the four-part form: trigger, identity, side effect, window. The day-one deliverable is not a patch. It is a one-page manifest that says what must be proven and what evidence will count.
Day two is for the deterministic replay. Build the smallest fixture that fires the target path twice with the same identity and records attempted entries, committed side effects, and duplicate suppression. Add the negative control for distinct identities. At the end of day two, the replay should fail against the unpatched or deliberately degraded implementation. If it does not fail, the fixture is not strong enough.
Day three is for the actual double-fire fix. Prefer the boring solution: claim before side effect, make the claim atomic, scope the idempotency key to the business identity, and record the loser path explicitly. Avoid fixes that depend on sleep intervals, local process memory, or log parsing. The patch should make the target replay pass and should not suppress unrelated identities. If the code cannot make those two statements true at the same time, the design is not ready.
Day four is for verifier integration. Wire the manifest into the experiment runner. Make verification_passed depend on required assertions, required artifacts, and forbidden events, not just exit_code. Add reason codes for missing_required_artifact, required_assertion_absent, forbidden_duplicate_side_effect, and command_failed. The output should make the old confusing state legible: command green, proof red, exact missing evidence named.
Day five is for hardening and ship criteria. Run the target eval, the negative control, the mutation check, and the broader regression suite. The ship line is narrow: exit_code=0, verification_passed=true, required assertions present, duplicate side effect absent, negative control intact, and mutation check capable of failing. Anything less is not a shipped fix. It is a green command wrapped around an unproven repair.
The diagnosis is blunt. If the eval passes with exit 0 but verification_passed=false, the system is probably running successfully and verifying unsuccessfully. That is not a small reporting bug. It means the experiment cannot prove it tested the double-fire fix. The correct response is to preserve the red proof bit, build a targeted replay, and make unrelated tests ineligible for fix-specific verification.
The five-day sprint is deliberately narrow because the danger is not lack of activity. The danger is activity that produces a pass-shaped artifact while the duplicate side effect remains live. The fix ships when the harness can replay the historical trigger, observe two attempts, prove one side effect, prove one suppression, preserve unrelated throughput, and fail when the idempotency guard is removed.
For teams that need this converted into an execution lane, the recommended internal sprint is None: no broad platform rewrite, no generic quality initiative, no ceremonial eval redesign. The work is one deterministic double-fire replay, one atomic guard, one evidence manifest, and one verifier that refuses to confuse a clean process exit with behavioral proof.
Five business days, fixed price, full runbook on delivery. Sample deliverables on the sprint page show exactly what you get before you commit.
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