An AI-generated summary of a paper is a starting point for judgment, not a substitute for it. The question worth asking is not “can I trust this,” which invites a yes-or-no answer that is always wrong, but “which specific parts of this need my own verification before I rely on them.” That list is short and consistent.
Numbers and statistics — always verify
Sample sizes, effect sizes, p-values, and percentages are exactly the kind of detail language models can misstate confidently. Check every number that will appear in your own writing against the original table or results section, not against a summary of it.
Causal language — read closely
Summaries frequently upgrade correlational findings into causal-sounding language, because causal phrasing reads more naturally than hedged phrasing. If a summary says a study “shows” or “demonstrates” that one thing causes another, check the original paper’s own language — many authors are far more careful than their summaries make them sound.
Scope and limitations — check what got dropped
Summaries compress, and what gets cut first is usually the population boundary, the setting, and the stated limitations — the exact details that determine whether a finding actually applies to your argument. A summary that omits “in a sample of undergraduate students” is not wrong, exactly; it is missing the detail that would have stopped you from over-generalizing it.
Attribution — confirm it’s the right paper
Confirm that a cited claim is actually attributed to the paper you intend to cite, not to a different paper it discusses, critiques, or builds on. This kind of misattribution is one of the most common and hardest-to-catch failure modes in AI-assisted literature work.
A working rule
Use an AI summary to decide whether a paper is worth reading closely — not as a replacement for reading closely the papers your argument will actually depend on.
Use the summary to plan reading, not to replace it
A good next step is to mark each sentence in a summary as a direct result, an interpretation, or background context, then verify the direct results first in the article. Tools that show a confidence rationale can help prioritize this audit; the research workspace presents it as a prompt for verification, not a verdict on whether a claim is true.