Conflict Detection

When the evidence disagrees, the worst thing an AI can do is quietly pick the side that fits its answer. A reader deserves to know the studies conflict — that fact is often the most important thing about the evidence. This skill makes the agent actively compare claims for contradiction and put the conflict on the table.

When to use

After claim-extraction, before writing any conclusion that leans one way. Feeds source-reliability, which decides how to weigh a conflict once it's found.

The rule

Compare claims that share a topic for directional contradiction. When two claims on the same thing point opposite ways, flag it — do not average it or drop one.

Check each pair of claims that are about the same topic for three kinds of conflict:

1. Directional — "increased" vs "decreased" (or "higher" vs "lower") on the same measure.
2. Effectiveness — "effective" / "benefit" vs "no effect" / "no significant difference" on the same intervention. Check the negated form first, so "not effective" is never misread as "effective".
3. Effect-size direction — a protective estimate (HR/RR/OR below 1) vs a harmful one (above 1) for the same intervention and outcome.

To decide two claims are "about the same topic", compare their topic fingerprint — the meaningful keywords with directional and filler words removed. Two claims only conflict if they genuinely share a subject; a difference on two different outcomes is not a conflict.