rlat-contradictions

Detect cross-document contradictions by applying geometry-based candidate generation to compare passages across your .rlat corpus. This surfaces incompatible claims that require human judgment to confirm the stance and resolve the inconsistency.

A consistency check for a .rlat corpus identifies cross-document passages likely to conflict using geometry-based candidate generation. It surfaces candidate pairs for human-in-the-loop judgment to confirm if claims truly contradict and what resolution applies.

No, you do not need external services. Auditing your .rlat corpus for inconsistent claims uses geometry-based candidate generation locally, requiring only local access to your .rlat knowledge model to surface incompatible claims.

No, you cannot fully automate the resolution of contradictions during a data audit. The process relies on human-in-the-loop judgment, enabling a user to decide if surfaced candidate pairs truly contradict and what resolution applies.

You should run a cross-document contradiction check during pre-deployment corpus audits. This ensures internal consistency by surfacing incompatible claims inside your rlat knowledge model before the corpus is deployed.