How AI is Used
How the summaries are made, what gets checked, and where AI can still go wrong.
The Short Version
Every summary here is written by a language model, and language models state things that are not true. The pipeline assumes that will happen. It restricts what the writing model is allowed to see, checks the finished text against quoted source material in code, and withholds anything that fails. None of this makes a summary certainly correct. Each article links to the paper so any specific claim can be checked in a few seconds.
How Hallucinations Are Mitigated
1. Nothing is invented at the source
Papers arrive from the official arXiv and Hugging Face APIs. No model is ever asked to recall interesting research, which is where invented citations come from. The model that chooses what to feature sees only real titles and abstracts, and returns identifiers that are checked back against that list. A paper that does not exist cannot enter the pipeline.
2. The model that writes never reads the paper
One model reads the paper and pulls out its results, each one required to arrive with a quote copied word for word from the source. Our pipeline string-matches every quote against the paper text and discards any result whose quote is not really there. A second model then writes the summary from the surviving facts alone, with no access to the paper. It cannot embellish source material it was never given. Prompts asking a model to be careful are advice it can ignore; withholding the text is not.
3. Finished text is checked, then withheld if it fails
Every figure in the finished summary must appear in the quoted facts, as must every model, dataset, and benchmark named. Sentences putting an opinion in the authors’ mouths must point at something the authors actually said. A separate model, usually from a different company, then reads the draft against the facts and hunts for claims they do not support. Each of these runs in code and has one outcome. A summary that fails any of them is discarded and the paper waits for the next run.
4. What the checks cannot do
Matching a quote proves the words are in the paper. It does not prove the summary drew the right conclusion from them, kept the surrounding context, or noticed the caveat two paragraphs later. Emphasis and framing are not checkable by code at all. Every article links to the paper and its PDF, and the honest use for this site is deciding what deserves your afternoon.
Limitations
A few things to keep in mind while reading:
- Code samples: The code on each article is an educational sketch of the core idea, not a production implementation. Expect to adapt it before running it anywhere real.
- Preprints are not peer reviewed: Most featured papers are preprints. Their results have not been through review yet and can change or fall apart under scrutiny.
- Diagrams show the concept: Flowcharts are generated from the paper's description of its own system. They illustrate the idea and are not a formal claims chart.