AI for Research That Shows Its Sources
An answer without a link is a rumour with good grammar.
Research is the use case where model hallucination stops being amusing and starts being professionally dangerous. The defence is procedural: cited search for anything factual, your own uploaded documents as the ground truth, and a second model reading the first one's conclusions. Clade puts all three in one thread.
A research loop that survives scrutiny
- Open with web search on so the first pass comes back with live, linked sources.
- Open the citations. Models occasionally attach a real-looking link to a claim it does not support.
- Upload the actual papers, reports or datasets — grounding the conversation in documents you control beats relying on recall.
- Switch models and ask the second one to attack the first one's conclusion. Agreement between labs is weak evidence; a specific rebuttal is useful.
- Ask what would falsify the finding. It is the fastest way to expose a conclusion built on one shaky source.
Where autonomous agents earn their keep
A literature scan is not one question, it is thirty. Handing that to an agent changes the economics: give it the topic and the shape of the output you want, and it plans the queries, works through them, and returns a structured brief with sources rather than a single paragraph.
It is not a replacement for reading the papers. It is a replacement for the two hours of finding out which papers to read.
Working with your own documents
- Upload PDFs, spreadsheets, CSVs and images — several at once.
- Ask cross-document questions: where do these two papers disagree, which dataset contradicts the stated conclusion.
- Use the code interpreter to actually run the statistics rather than asking a model to estimate them.
- Long-context models take an entire paper or report in one pass, so nothing gets chunked away.
- Publish the finished analysis as an artifact and share it with a link.
Failure modes to design around
- Fabricated citations — the single most common and most damaging error. Open every link.
- Confident summarisation of a paper the model has only partly ingested. Upload the PDF rather than naming the title.
- Training cutoffs. Anything recent needs search, not recall.
- Sycophancy: models tend to agree with the framing of your question. Ask the opposite question and see if the answer flips.
- Statistical hand-waving. If a number matters, compute it.
Models you can use for this
MiMo-V2-Flash256K
DeepSeek-V3.2 (Reasoning)128KDeepSeek-V3.2128K
Qwen3-Max256KQwen3.5-Plus1M
Command A Reasoning256KCommand A256K
MiniMax M2197KFrequently asked questions
Which AI gives real citations?
Clade's built-in web search is backed by Perplexity Sonar models and returns linked sources with the answer. Always open them — citation presence is not citation accuracy.
Can AI do a literature review?
An agent can produce a structured first-pass scan with sources, which is a genuine time saver. It cannot replace reading the papers or exercising judgement about which findings are credible.
Can I upload research papers and datasets?
Yes — PDFs, spreadsheets, CSVs and images, several at once, with cross-document questions supported. The code interpreter can run real statistics on uploaded data.
How do I stop AI from making things up?
Ground it: turn on web search, upload the source documents, ask for citations, and verify them. Then run the conclusion past a second model from a different lab. That combination catches most fabrication.
Is my research data private?
Clade never uses your conversations or uploads to train models, data is encrypted, and you can delete anything permanently. For data under institutional or regulatory restrictions, check your own compliance requirements first.
Keep reading
Research with sources you can open
Cited search, document upload and multi-model cross-checking in one thread. Start free.