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Evaluate an AI peer-review system with a held-out set of real manuscripts and independently adjudicated error cases, then report recall, precision, false alarms, omission detection, claim-and-evidence localization, severity, uncertainty and reproducibility separately. A single benchmark score cannot show that a system is safe for editorial or research use.
The strongest evaluation combines deliberately inserted, consequential errors with authentic reviews, valid claims and legitimate criticism. It records exactly what the system saw, controls model and tool conditions, and preserves an evidence trail so another team can reproduce every result.
Decide what “catches errors” means for your workflow
An AI reviewer may be asked to check several different failure modes. Define them before collecting examples or tuning prompts; otherwise a system can look successful simply because the test favors the errors it handles well.
Set the claim-threatening error categories
- Factual or evidential support: a conclusion is not supported by the cited result, source or data.
- Methods: the design, sample, control, measurement or analysis cannot support the stated inference.
- Numerical and statistical consistency: values, denominators, effect sizes, uncertainty intervals, tests or reported conclusions conflict.
- Omitted information: a disclosure, limitation, exclusion, competing explanation or required evidence is absent.
- Review integrity: the system invents a flaw, misstates what a paper says or treats a legitimate criticism as hallucinated.
Specify the manuscript types and disciplines in scope. A test limited to open-access psychology papers cannot establish performance on clinical trials, engineering reports, economics papers or unpublished submissions.
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Define the unit of evaluation
Choose whether the unit is a claim, paragraph, review comment, paper or paper-review pair. Claim-level scoring is useful for detection and localization; paper-level decisions matter for editorial triage. Keep both levels if a single flagged claim can trigger a decision about an entire manuscript.
Build an independently adjudicated ground-truth set
Ground truth must come from evidence and expert adjudication, not from the AI system’s confidence or self-critique. Preserve each source manuscript and annotate the exact claim, the relevant evidence and the reason the claim is valid or erroneous.
Include controlled error cases
Create carefully controlled variants in which one consequential methodological, statistical or evidential problem is inserted while the rest of the manuscript remains unchanged. Record the original version, the edited version, the affected claim and the expected reviewer action. Include both explicit contradictions and omissions. An omission case should identify what information is missing and why that absence threatens interpretation; it should not merely be a stylistic preference.
Use negative controls
Include valid claims, correct calculations, appropriate limitations and legitimate criticism. These controls reveal whether the system flags ordinary scientific disagreement as an error. Label the severity and the minimum evidence needed to support each decision.
Adjudicate disagreements
Have qualified reviewers independently label cases, then resolve disagreements under a documented procedure. Keep the adjudication record, decision rules and any changes to labels. If experts disagree because a claim is genuinely ambiguous, retain an “indeterminate” category rather than forcing a binary truth label.
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Keep development data separate from the final test
Use one development set for prompt design, retrieval configuration and system tuning, and a separate held-out evaluation set for the final measurements. Do not repeatedly inspect held-out outputs while changing the system; that turns the test into another development set.
Public planted-error data can leak into later training corpora. The Dawes Institute’s August 2026 release warns that models trained after publication may have seen its ground truth. Protect private cases where possible, rotate test items, or report the potential contamination explicitly. A score on a public set is conditional on that set, not proof of performance on new manuscripts.
Score each outcome separately
Report a table of outcomes rather than collapsing everything into one accuracy number.
| Measure | Definition | Why it matters |
|---|---|---|
| Recall (sensitivity) | Known consequential errors correctly flagged ÷ all known consequential errors | Shows how many dangerous problems the system misses. |
| Precision | Correct error flags ÷ all flags the system makes | Shows how much editorial time is spent on valid findings rather than false alarms. |
| False-positive rate | Valid claims incorrectly flagged ÷ all valid control claims | Measures unjustified criticism of sound research. |
| Omission recall | Omissions correctly identified ÷ all annotated omissions | Separates missing-information detection from visible contradictions. |
| Localization | Flags that identify the affected claim and supporting evidence correctly ÷ all relevant flags | Tests whether a reviewer can act on the output instead of searching the paper again. |
| Severity-weighted recall | Weight each error by its preassigned consequence, then calculate detected weight ÷ total weight | Prevents many minor catches from masking a missed fatal error. |
| Category results | Separate results for methods, statistics, factual support, omissions and other predefined classes | Exposes strengths hidden by an aggregate score. |
Define what counts as a correct flag before scoring. For example, require a quotation or location for the claim, a reference to the relevant data or method, and a short explanation of the contradiction. A vague statement that “the analysis seems weak” should not receive full localization credit.
Test authentic review contexts, not only planted mistakes
Planted errors provide controlled labels, but authentic reviews contain ambiguity, incomplete information and legitimate disagreement. Include real papers and reviews when permissions allow. Ask adjudicators to distinguish a hallucinated criticism—an invented problem or a claim the paper does not make—from a valid critique of a real limitation.
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HalluPeer, a peer-review-specific preprint dated September 3, 2026, aligns paper content with human reviews and hallucination-injected reviews. Its reported experiments cover 12,000 papers and 38,000 reviews and find that existing detectors struggle to separate hallucinations from legitimate critique. Treat those results as preprint evidence, not as a universal estimate, and reproduce the distinction in your own test.
Run systems under controlled, comparable conditions
When comparing systems, hold the task and information conditions constant. Record the model and version, prompt, temperature or other inference settings, context window, retrieval corpus, browsing and code-execution access, tools, time limits and output format. If one system can retrieve the full cited literature while another receives only the manuscript, their scores are not directly comparable.
Use a reference review process
Have expert reviewers or an adjudication panel assess the same cases. Blind them to system identity where practical. Human performance is not an infallible gold standard, but it provides a useful reference for workload, disagreement and the kinds of errors that remain difficult for people as well as models.
Compare operating points, not just default settings
A system may trade recall for precision through a confidence threshold or review mode. Report the setting used and, when relevant, show results at several operating points. Do not select the most favorable threshold after inspecting the held-out labels.
Quantify uncertainty and generalization
State uncertainty intervals or another design-appropriate uncertainty estimate for every major metric. Items from the same paper are not independent: a single difficult manuscript can contribute many correlated claims. Stratify or cluster the analysis by paper, authoring study or other sampling unit instead of treating every claim as unrelated.
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NIST AI 800-3 (Keller and colleagues, 2026) distinguishes benchmark accuracy—performance conditioned on a fixed benchmark—from generalized accuracy on potential similar items. Its guidance describes generalized linear mixed models, variance decomposition and item-difficulty analysis for estimating the latter. Report the fixed-set result you observed and separately explain what population of future manuscripts you believe it represents.
Show stability across repeated runs when the model is nondeterministic. A system that catches 80% of errors once and 55% on a rerun may be unsuitable for a workflow that relies on consistent triage, even if the average is acceptable.
Preserve an audit trail and an evidence chain
Store the test-set version, manuscript hashes or identifiers, model and prompt versions, inference settings, retrieval sources, tool calls, run date, raw outputs, adjudication decisions and scoring code. Keep enough information to reconstruct the exact input supplied to the model; silently changing a system prompt or retrieval index invalidates a comparison.
For systems that produce research artifacts, verify the output’s claims as well as its error flags. Google Research’s experimental Science One and Chain-of-Evidence framework states that “every claim in a research artifact must carry a recorded evidence chain (completeness), and each chain must genuinely support the claim it is attached to (correctness).” Apply those two checks separately: every important statement should point to evidence, and the linked evidence should actually entail or support it.
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What existing benchmark results do—and do not—show
| Source and date | Design and reported figures | How to interpret it |
|---|---|---|
| Dawes Institute, AI Peer Review Benchmark, 2026 | 100 deliberately inserted methodological and statistical errors in 10 open-access psychology papers. The best single tested configuration caught 71/100; the union of 14 configurations caught 93/100. All seven errors missed by every configuration were omissions. | Useful evidence that configuration and error type matter. These are results from that project’s setup, not a universal performance guarantee. Public release in August 2026 creates possible training-data contamination for later models. |
| PROBE, Zhang and colleagues, Findings of ACL 2026 | 12,000 test cases across summarization, question answering and style transfer, decomposed into claim decomposition, evidence finding, evidence evaluation and hallucination localization. | Adjacent evidence for evaluation design. The authors identify evidence finding as a central weakness; it is not a direct test of every peer-review system. |
| HalluPeer, Lin and colleagues, preprint, September 3, 2026 | 12,000 papers and 38,000 reviews, including human reviews and hallucination-injected reviews, with detection, classification and localization labels. | Directly relevant to review hallucinations, but the results are from a preprint and should be treated as provisional. |
| NIST AI 800-3, 2026 | Study of 22 API-access frontier large language models on three popular benchmarks; formal treatment of fixed-benchmark and generalized accuracy. | Supports uncertainty and generalization analysis rather than a peer-review pass threshold. |
| SCORE, Center for Open Science, outcomes reported in 2026 | 3,900 claims evaluated with 865 collaborators and nine papers. Reproducibility, robustness and replicability were treated as distinct dimensions and were only modestly correlated. | Supports measuring research credibility in multiple dimensions instead of treating error-spotting recall as truth. |
A reproducible evaluation procedure
- Write the evaluation charter. Name the decision the system supports, disciplines and manuscript types, error categories, severity levels, acceptable evidence and the human escalation path.
- Sample source papers. Define inclusion rules and document the sampling frame. Preserve originals and permissions.
- Create labeled cases. Add controlled contradictions and omissions, retain valid claims and legitimate criticisms, and annotate claim locations and evidence.
- Adjudicate independently. Use qualified reviewers, document disagreements and freeze the labels before system testing.
- Split the data. Put tuning cases in development and reserve a held-out set. Protect private cases or rotate public cases to reduce contamination risk.
- Freeze the system configuration. Record model version, prompt, tools, retrieval sources and inference settings before the final run.
- Collect structured outputs. Require each finding to identify the claim, evidence, error category, severity and explanation. Preserve raw output as well as parsed fields.
- Score without post-hoc relabeling. Calculate recall, precision, false positives, omission recall, localization and severity-weighted results by category and by paper.
- Estimate uncertainty. Use intervals or mixed-effects methods appropriate to clustered claims, and report run-to-run variation.
- Audit a sample manually. Inspect misses, false alarms and apparently correct flags. Confirm that cited evidence supports the finding and that no formatting parser hid a failure.
- Report generalization limits. Describe which future manuscripts the sample can represent, possible benchmark leakage and any disciplines or languages not tested.
How to compare two or more systems fairly
Use a comparison matrix that includes:
- Recall and precision for each error category and severity level.
- Detection and localization of omissions, not only explicit contradictions.
- False-positive burden on valid claims and legitimate criticism.
- Performance by discipline, manuscript type, language and paper difficulty.
- Evidence quality: whether findings quote the right claim and link to supporting data or references.
- Uncertainty, repeatability across runs and sensitivity to prompts or retrieval settings.
- Tool and retrieval access, context length and time or cost limits.
- Whether the test set is private, held out, public or potentially contaminated.
- Reproducibility of the complete evaluation, including scoring code and adjudication.
Do not rank systems by a single aggregate score when these conditions differ. A system with higher recall but a large false-positive burden may be preferable for a high-risk screening stage, while a precision-oriented system may fit an editor who has limited time for follow-up. Make that trade-off explicit rather than labeling one system universally best.
Choose a deployment rule from consequences, not a universal pass mark
No reviewed source establishes a universal passing score or deployment threshold for AI peer-review systems. Set criteria for the intended workflow: the harm of a missed error, the cost of investigating a false alarm, the diversity of manuscripts, the availability of expert oversight and whether the system can block publication or only suggest questions.
For a high-consequence decision, require human verification of every flagged claim and prohibit the system from silently clearing an unflagged manuscript. Define escalation rules for low-confidence or indeterminate cases. Re-evaluate after model, prompt, retrieval or manuscript-population changes; a previously measured score does not automatically transfer to a new configuration.
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Common evaluation failures and their fixes
| Failure | Why it misleads | Fix |
|---|---|---|
| Testing only obvious contradictions | Visible errors are easier than missing disclosures or unsupported inferences. | Add omissions and report omission recall separately. |
| Using the model’s self-judgment as truth | A confident explanation can still cite irrelevant or invented evidence. | Use independent adjudicators and preserved source evidence. |
| One accuracy number | High scores can hide missed severe errors or excessive false alarms. | Report recall, precision, false positives, localization, severity and categories. |
| Reusing the public benchmark for tuning | Repeated exposure causes overfitting and possible training contamination. | Keep a held-out or private test set and disclose release dates. |
| Treating claims within one paper as independent | Correlated items make uncertainty look smaller than it is. | Cluster or stratify by paper and use an appropriate mixed-effects analysis. |
| Comparing unequal information access | Retrieval, browsing and code execution can dominate model differences. | Match access and settings, or report the differences as part of the result. |
| Accepting vague criticism | Editors cannot act on a finding that lacks a claim location and evidence. | Score localization and evidence support explicitly. |
| Assuming error detection equals research truth | Credibility also depends on reproducibility, robustness and replicability. | Measure those dimensions separately where the workflow requires them. |
What a credible evaluation report should contain
A reader should be able to answer five questions from the report:
- What was tested? Describe the manuscript population, disciplines, claim unit, error taxonomy, severity rules and sample construction.
- What did the system see? List model and version, prompt, tools, retrieval sources, settings and run dates.
- How was correctness decided? Explain adjudicator qualifications, independence, disagreement handling and evidence requirements.
- What were the outcomes? Give counts and uncertainty for recall, precision, false positives, omissions, localization, severity and each subgroup.
- How far can the result travel? State the held-out design, possible contamination, clustered sampling, untested disciplines and the human oversight required.
This format turns an impressive demonstration into an auditable measurement. It also makes failure visible: a system may be strong at finding present-but-wrong numbers yet weak at noticing what a paper never disclosed, or accurate on a fixed benchmark while unstable on new manuscripts.
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