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Narrow path discovery is useful before editing, but the requested work is now a post-refactor verification receipt. Listing relevant paths does not prove behavior was preserved. Keep before/after evidence, impacted tests and their results for the specific changed interface.

Symbol searches locate consumers of a changed function, but the question concerns demonstrating behavioral parity after the refactor. Search results alone cannot show whether new behavior matches the accepted baseline. Preserve representative inputs, outputs and regression results.

File summaries help future agents navigate a repository, but they are not a substitute for test evidence of the refactor. Maintain a scoped behavior comparison and recorded regression checks so reviewers can assess what actually changed.

A correction record supports audit and evaluation improvement. The required action is “Validate the output against required evidence and send unresolved gaps for review”; the other measure addresses a separate concern. Human review is most valuable for the cases where several interpretations remain possible or the consequences of an incorrect claim are high, not as an uncalibrated popularity vote.

Correlated model errors can produce unanimous wrong answers. Although useful elsewhere, it is not the direct mechanism to calibrate review against observable evidence not model confidence.

A correction-feedback loop helps future evaluation improve, but it cannot justify accepting this financial output today when source pages are missing. Check the required evidence for the extracted amount and escalate unsupported fields; a 99% model confidence claim does not replace those document checks.

A numeric confidence score is only useful if its relation to observed correctness is known. That addresses a different part of the system, not the requirement to allocate review thresholds according to consequence.

Reviewer decisions are valuable labelled examples. This capability does not directly allocate review thresholds according to consequence. Feeding prior corrections into future evaluations is valuable, but it does not immediately set an appropriately stricter review threshold for potentially harmful medical recommendations.

Monitoring distribution drift helps when a classifier begins seeing unfamiliar inputs, but the policy question contrasts low-impact tags with consequential medical recommendations. Even on a familiar distribution, high-consequence advice needs stricter review and escalation thresholds than routine tagging; severity should drive the initial policy.

Logging corrected cases can improve a model in later releases, but it does not itself define safe automatic approval of today's routine summaries. Use measured quality thresholds and sample a representative subset of auto-approved low-risk outputs so performance can be checked continuously while manual review decreases.

A numeric confidence score is only useful if its relation to observed correctness is known. This alternative does not establish how to reduce routine review while monitoring reliability in the stated workflow.

Strict human review is suitable for high-consequence recommendations, but forcing it on every low-risk summary defeats the requested reduction in routine workload. Calibrate thresholds using measured outcomes and retain representative post-approval sampling, reserving intensive review for uncertainty, elevated impact or detected drift.

Shared source error can produce consistent but false model agreement. Evidence must be independently checked against reliable references before a consensus is treated as factual.

A correction record supports audit and evaluation improvement. That addresses a different part of the system, not the requirement to avoid treating model consensus as ground truth.

Evidence validation is necessary for any accepted claim, but this choice alone does not explain the failure of agreement across three models: all used the same flawed source. Trace the shared primary evidence and independently verify it rather than interpreting repeated model consensus as separate corroboration.

An uncalibrated threshold cannot predict operational error rates. Representative labeled cases must establish how confidence relates to actual correctness before review policy uses that threshold.

Reviewer decisions are valuable labelled examples. The decisive requirement here is to calibrate confidence thresholds against real outcomes. A correction feedback loop helps future improvement, but without calibration data it does not establish which present confidence cutoff is safe. Measure actual correctness by score band first.

A threshold transferred from another task may have different error prevalence and consequences. Measure accuracy and calibration in representative labeled score bands before selecting the policy cutoff; monitor its false positives and false negatives after rollout.

Primary-source verification strengthens factual reports, but the reviewer queue mixes minor formatting flaws with serious authorization defects. The immediate fix is severity-based triage using business impact and uncertainty, prioritizing harmful access-control problems. Source checking remains valuable within that ordered review, not a substitute for prioritization.

Reviewer decisions are valuable labelled examples. Although useful elsewhere, it is not the direct mechanism to prioritize human review by risk rather than arrival order.

A correction record supports audit and evaluation improvement. This alternative does not establish how to prioritize human review by risk rather than arrival order in the stated workflow.

Reviewer decisions are valuable labelled examples. The required action is “Original value, corrected value, reviewer identity and supporting evidence”; the other measure addresses a separate concern. The audit needs to distinguish a supported address repair from a silent alteration of customer data.

A stricter approval threshold for consequential medical recommendations is unrelated to the missing audit trail for a corrected address. This case needs the original and corrected values, the reviewer, timestamp and supporting account or document evidence, so future decisions can explain the change and replay it safely.

Proportionate prioritization focuses human effort on consequential failures. This capability does not directly record accountable human correction decisions. General review triage does not preserve who corrected the address and why. Auditability needs before-and-after values, reviewer identity and supporting evidence.

A fresh evidence check is useful when an assertion is poorly sourced, but the classifier's error rate has increased because handwritten scans differ from its typed-form evaluation set. Monitor accuracy by input type, increase manual review for the shifted population and test model improvements on representative handwriting.

Correlated model errors can produce unanimous wrong answers. This alternative does not establish how to adapt review to changing input distributions in the stated workflow.

The same uncertainty can have different impacts. The decisive requirement here is to adapt review to changing input distributions. An appropriate safety threshold matters, but handwritten scans introduce a new input distribution whose error rate must be monitored and evaluated rather than assuming the prior model still performs the same.

A correction record supports audit and evaluation improvement. Although useful elsewhere, it is not the direct mechanism to use human corrections to drive continuous evaluation.

Escalating medical recommendations under stricter thresholds is a risk-control policy, but it will not address repeated extraction mistakes that reviewers fix every week. Capture correction categories and evidence in a versioned evaluation set, then measure whether a documented model or workflow change reduces those recurring errors.

Sampling monitors drift while reserving intensive review for difficult cases. The direct remedy is “Feed classified correction cases into a versioned evaluation and improvement cycle”, which the alternative does not itself accomplish. Convert recurring human corrections into versioned regression tests and evaluate the next model or rule change against them.

A correction record supports audit and evaluation improvement. That addresses a different part of the system, not the requirement to confirm human review authority before sensitive release.

Checking underlying evidence can improve a claimed legal review, but the release decision also requires proof that the designated approver actually authorized this exact artifact. A chat sentence with no attributable actor does not establish approval. Require a verifiable reviewer identity, decision and version-specific authorization record.

Revalidating factual evidence helps assess the report content, but does not prove the claimed legal approver had authority or reviewed the released revision. Verify the identity, role, approval decision and artifact hash or version. A source citation cannot substitute for a recorded authorization event.