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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsFix citation failures by checking three things separately: whether the reference resolves, whether its source is relevant, and whether the cited evidence supports the claim. A working URL alone proves none of the latter two. Build a source registry, bind citations to retrieved passages, validate links mechanically, and assess each claim against its evidence before the answer is published.
Diagnose which part of the citation failed
Treat a citation as a relationship among a claim, a source, and a specific piece of evidence—not just as a link at the end of a sentence. Test these dimensions independently:
- Reference validity: Does the citation identify a source that exists and can be opened?
- Relevance: Is that source about the subject of the claim?
- Support: Does the cited passage substantiate the entire claim as written?
A link can pass the first check and fail the other two. A relevant source can also be cited for a detail it never establishes. Logging the failure type prevents a superficial URL fix from leaving the underlying factual error in place.
Build a source registry from retrieved evidence
At retrieval time, create a trusted record for every source the model is allowed to cite. Keep the retrieved evidence separate from any citation text generated by the model.
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- Assign a canonical source ID.
- Store the final URL, title, source type, and retrieval timestamp.
- Keep the exact passages supplied to the generation step.
- For multi-turn or cached systems, record whether each passage was fetched for the current request or served from cache.
NVIDIA’s documented research-agent design uses a per-session source registry for URLs and citation keys returned by retrieval tools, then checks report references against that registry. The key engineering principle is to allow citations only to evidence the system actually retrieved or the user supplied.
Generate citations from trusted metadata
Have the model attach internal source IDs to individual claims. Render the public-facing title, URL, and other citation metadata from the registry, not from model-generated strings. This avoids invented links and mismatches between a real URL and a fabricated title or date.
If a claim has no source ID tied to retrieved evidence, do not let the model fill the gap with a plausible-looking reference. Retrieve a source and check it, or mark the claim as unverified, revise it, or omit it. Anthropic’s search-result format documentation shows how source URL and title metadata can accompany result text so citations can be linked to supplied material.
Validate links without confusing link health with evidence quality
Run deterministic checks on citation IDs and URLs before publishing:
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- Confirm each cited source ID exists in the request’s registry.
- Check that the URL is well formed and matches the registry entry.
- Check whether the page resolves; retain the result and time of the check.
- Apply URL normalization narrowly. Accept only variations your system can safely map to the same source, and reject ambiguous matches.
NVIDIA describes exact and normalized URL matching, as well as constrained prefix, child-path, and query-subset matching. Its design removes unmatched citations and records an audit reason. A match rule that is too permissive can silently attach a citation to the wrong page, so retain the matched registry record and the rule that accepted it.
For a dead link, distinguish a source that was retrieved and later moved from a URL with no evidence of ever having existed. The 2026 preprint describing urlhealth uses URL-liveness checks and Wayback Machine information to classify stale versus likely fabricated URLs. A replacement link is not a repair by itself: retrieve the replacement and re-check the claim against its evidence.
Check support one claim and passage at a time
Break long sentences and answer paragraphs into atomic claims that can be checked independently. Compare each claim with the cited passage, rather than relying on a source’s title, abstract, or general subject. A passage must support the whole claim. If it supports only one clause, narrow the wording, add evidence for the missing part, or remove the unsupported detail.
NIST’s evaluation probes distinguish three useful questions: faithfulness (does the source support the claim?), completeness (does the claim preserve the source’s full message?), and sufficiency (does the evidence carry the burden of the claim?). These checks catch different errors: an unsupported statement, a cherry-picked description, and an overconfident conclusion based on too little evidence. See NIST’s probe description.
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Google Cloud’s grounding check documentation describes linking claims to cited chunks and assigning support scores. Its guidance says fully grounded claims must be entailed by supplied facts. Such a score can help route claims for review, but it is not proof that the source itself is true or that an automated evaluator is infallible.
Choose an explicit repair for each failure
Do not decorate an unsupported answer with a different citation. Record what happened to the claim and evidence:
| Failure | Repair | Disposition to record |
|---|---|---|
| URL does not resolve, but the source was retrieved | Find a current authoritative location, retrieve it, then repeat the claim-to-passage check. Do not substitute a search snippet. | Replaced, revised, or removed |
| URL does not resolve and there is no evidence the source existed | Remove the citation; retrieve genuine evidence before retaining the claim. | Removed or revised |
| URL resolves but source is irrelevant | Retrieve a source that directly addresses the claim, or remove or narrow the claim. | Replaced, revised, or removed |
| Relevant source only partly supports the claim | Narrow the sentence, add evidence for the unsupported part, or remove that detail. | Revised or removed |
| Evidence is weak or sources conflict | Qualify the claim, describe the disagreement when it matters, or abstain. | Revised or removed |
A confidence label is useful only if it is calibrated against observed performance. Do not assign arbitrary confidence values as a substitute for evidence review.
Test citation behavior with regression cases
Maintain a fixed evaluation set that tests both what the agent answers and which source it uses. Include:
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- Competing documents with similar facts, so the test can detect a plausible answer attributed to the wrong source.
- An updated source alongside an outdated one.
- Questions for which none of the available sources contains the answer.
Microsoft’s knowledge-grounding scenario library recommends unique markers and source-attribution checks; it cautions that an answer from the wrong source is still a grounding failure even if the answer happens to be correct. Track link validity, relevance, entailment, completeness, and sufficiency separately across the same test set. More retrieval is not automatically better: a 2026 preprint reports an approximately 42% average drop in fact-check accuracy as tool calls rose from 2 to 150 for two tested frontier models, under that paper’s evaluation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep an audit trail that explains the decision
For each claim, preserve the claim text, source ID, exact supporting span, URL-check result, semantic verdict, rationale, evaluator or model version, and final disposition. This makes it possible to trace a failed citation from the published sentence back to the evidence and checks that allowed it through.
NIST describes structured audit trails that map agent decisions to evidence, while NVIDIA documents logging citation-verification decisions. Use deterministic checks for provenance and URL syntax; use rubric-based semantic evaluation and human review for consequential claims. NIST’s evaluation-probe project and NVIDIA’s research-agent blueprint describe these complementary parts of the process.
Interpret published citation-error figures narrowly
Reported rates vary by benchmark, system, and evaluation method; none establishes a universal citation-error rate. The 2026 preprint Detecting and Correcting Reference Hallucinations in Commercial LLMs and Deep Research Agents reports that 3–13% of citation URLs were hallucinated and 5–18% did not resolve in its DRBench and ExpertQA evaluations. In experiments with urlhealth, the authors report a 6–79× reduction in non-resolving citation URLs, to under 1%; effectiveness depended on the model’s tool-use ability. These are study-specific results, not expected outcomes for another agent.
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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →The 2026 preprint Cited but Not Verified reports 39–77% factual accuracy for evaluated systems even where link validity exceeded 94% and relevance exceeded 80%, under that paper’s benchmark and evaluation method. The figures illustrate why link checks and relevance checks cannot stand in for claim-level support checks. Google Cloud says its grounding check is designed for latency below 500 ms; that is a vendor-specific documented characteristic, not a general latency benchmark for grounding systems.
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