- Read in this order: abstract, figures and tables, conclusions, limitations, and the methods last.
- Ask: what exactly do they claim, what was it compared with, what was it measured on, can it be repeated and does it matter to me.
- An arXiv preprint is a pre-review work, so treat its results with care.
Scientific papers about AI look intimidating: dense, full of formulas, written for specialists. And yet most of the valuable information can be extracted from them without deep mathematics, if you know in which order to read and what to ask. This guide gives a simple scheme that you will apply to any paper, from classic works to the newest arXiv preprints.
Reading order
Do not read from the first sentence to the last. Start with the abstract, which says what was done and what came of it. Then skim the figures and tables, because that is where the concrete numbers are. Next read the conclusions and the section on limitations, if there is one. Only at the end, if the work is still important to you, go into the description of the method and the technical details. This order lets you decide after ten minutes whether it is worth reading on.
A good example is the abstract of Attention Is All You Need, the paper that introduced the Transformer architecture. The authors write what they propose (a network based solely on attention mechanisms), what they tested it on (two machine translation tasks) and what the result is (28.4 BLEU on the WMT 2014 English-to-German translation task and 41.8 on English-to-French after 3.5 days of training on eight GPUs). From a single paragraph you know what they claim, what it was measured on and what it cost.
Five questions for every article
Ask five questions and look for the answers in the text. First: what exactly do the authors claim? In one sentence, with no adjectives. Second: what was it compared with? A result without a reference point means nothing, so check which baselines were chosen and whether they are fair. Third: what was it measured on? Which dataset, which metric, how many cases. Fourth: can it be repeated? Look for code, a description of settings and information on cost. Fifth: does it matter to me? A work can be excellent and its result useless in your application.
| What you check | Where to look | |
|---|---|---|
| 1. Claim | What exactly they claim | Abstract, conclusions |
| 2. Comparison | What the result is set against | Results tables, the baselines section |
| 3. Measurement | Dataset, metric, number of cases | The experiments section |
| 4. Repeatability | Code, settings, cost | Appendix, repository link |
| 5. Relevance | Whether it concerns my task | Limitations, applications |
A preprint is not a verdict
Most AI papers first land on arXiv, a repository where authors publish texts before review or in parallel with it. It is a great source of current knowledge, but it means the result has not yet been independently assessed. So check whether the work has a version published at a conference or in a journal, whether later versions fix mistakes and whether other teams confirmed the results. Aggregators such as Daily Papers on Hugging Face help to find popular works, but popularity is not proof of quality. Watch for claims like “best result”: ask best at what and on which dataset.
Red flags
A few signals should raise your vigilance. No comparison with a strong baseline. A result only on one in-house dataset. No information on cost and settings. Conclusions bigger than the evidence, for example a general claim about “reasoning” drawn from a single test. No limitations section. None of these signals disqualifies a work, but each is a reason to use its results more carefully. Remember yourself too: it is easy to believe a result that confirms what you want to hear.
- 01AbstractWhat was done and what came out.
- 02FiguresNumbers, not descriptions.
- 03ConclusionsWhether the evidence supports them.
- 04LimitationsWhat the authors themselves caveat.
- 05MethodOnly if the work matters to you.
- 06NoteThree sentences in your own words.
A note after reading
The best test of understanding is a short note in your own words: what they did, what it was measured on, what follows and what is not known. Three sentences are enough. Such a note also becomes a knowledge base you return to in half a year when somebody asks how you know something works. Examples of well-described classic works are in the article Three papers worth knowing, and more materials in the Research and papers hub.
Last checked: 20 October 2026. The reading principles are lasting; specific works and numbers are described according to their abstracts.