aiDex for Researchers: Cross-Check Citations With a Judge
Put the source in the chat, then let a panel flag the references that do not hold up.
TL;DR
Researchers use aiDex to catch fabricated or misattributed citations before they reach a draft. Drop the real source into the chat so every model reads the same text, use Compare to gather independent readings, then use Judge to check each claim against the uploaded source and flag the ones that do not match. The panel narrows your verify list; you still open the source for the final call.
Why do AI models invent citations?
Language models predict likely text, so they can produce a reference that looks right, plausible authors, a believable title, a real-sounding journal, while pointing to a paper that does not exist or does not say what was claimed. Fabricated and misattributed citations are a documented and growing problem in AI-assisted research, and one confident wrong reference can undermine a whole section.
The fix is grounding. When a model has to answer from a source you provide instead of from memory, it has far less room to invent. aiDex is built for exactly that: drop the paper, dataset note, or report into the chat and every model in the conversation reads the same file (DOCX, PDF, MD, and txt are all fair game). The check stops being "what does the model recall" and becomes "what does this text actually support."
How do I cross-check AI citations with a panel?
Put the sources in the chat, then run two modes in sequence. First use Compare so several models read the same uploaded source and each one states, independently, what the text supports and which claims lean on which reference. Then use Judge to line those readings up against the source and flag any claim the paper does not back, plus any reference that cannot be matched to the file you uploaded.
Because the source sits in the chat, a model cannot quietly swap in a citation from memory: the comparison is against the text in front of it. Open aiDex, attach the sources, and you have a repeatable check instead of a one-off guess. This is the citation-safety layer on top of the wider multi-model AI workflows pattern.
How does Judge catch a fabricated reference?
Judge reads the candidate answers and the source together, scores each claim for support, and calls out where the models disagree. A reference that two or three models cannot locate in the uploaded paper, or that one model "remembers" while the others do not, rises to the top of your verify list. Disagreement is the signal: it tells you exactly which lines to open the source for.
Judge does not certify a reference as genuine, and it should not. It narrows a long bibliography down to the handful of suspect entries a human then confirms against the original. That is the honest promise here: less time hunting, a smaller set to check by hand, and a clear reason each item was flagged. It pairs naturally with getting a consensus answer from several models.
Can I keep unpublished or confidential research private?
Yes. Run the panel on local models with Ollama, or bring your own keys. Use your own provider keys or the ones we manage, and pick the models you want. For a manuscript under embargo or raw data you cannot share, keep the models fully local so nothing leaves your machine. For sources that are already public, mix a local model with a frontier model like Claude Opus 4.8 or Gemini 3.1 Pro, both of which read very long documents in a single pass, which is handy when the "source" is a 60-page report.
How do I turn verified notes into a literature summary?
Send the checked claims down a Pipeline. Draft writes the summary, Critique challenges every line that is not backed by a flagged-clean source, Revise tightens it, and Polish formats the references. Because only claims that survived Judge enter the Pipeline, the summary starts from vetted ground rather than hopeful memory. For a review you repeat every week, keep the same panel open as a Team so the models, and your standards, stay consistent. If you already lean on reviewing documents with a team of models, this is the same habit aimed at the reference list.
The aiDex Team · Multi-model AI platform
aiDex is a multi-model AI platform that lets you query several AI models at once, compare their answers, run consensus picks, and chain models in pipelines or open team chats. Use your own provider keys or the ones we manage, and pick the models you want.
Frequently asked questions
Can aiDex guarantee a citation is real?
No. aiDex flags citations its models cannot match to the source you uploaded, which narrows your verify list. A human still confirms each flagged reference against the original. Treat the panel as triage, not proof.
Why upload the source instead of just asking the model?
Uploading grounds the answer in the text in front of the model, not its memory. That is what lets Judge check a claim against the actual paper and catch a reference the model would otherwise invent. Every model in the chat reads the same file.
Which mode catches misattributed citations?
Judge. It reads the candidate answers and the source together, scores each claim for support, and flags references the models cannot locate in the uploaded file or that only one model recalls.
Can I check confidential or unpublished research?
Yes. Run the panel on local models with Ollama so nothing leaves your machine, or bring your own provider keys. Keep embargoed manuscripts and raw data local; use frontier models only for sources that are already public.
How many models should I put on the panel?
Two or three is usually enough for cross-checking. Different models catch different errors, and a small panel keeps cost and reading time down. Add a fourth only when a claim stays contested after the first pass.
Keep reading
Multi-Model AI Workflows: Why Query All Models at Once (2026 Guide)
One model is one opinion. Here is how to query several at once and get a better answer.
How to Get a Consensus Answer from Several AIs
Why a synthesized answer from several models beats one model on the questions that matter, and how to get one in two clicks.
How to Review a Document with an AI Team
Upload a file, let a panel of models read it together, and turn their flagged issues into an accepted set of edits.
aiDex for Analysts: Two Models Read, One Decides
Two models read the same data independently, a third reconciles the gap: a practical setup for analysts who have to defend the number.