SAFE-FEC: Semantically Constrained Adversarial Frontier Evolution for Factual Error Correction

The difficulty of factual error correction (FEC) is not only how to repair false claims, but how to construct errors that meaningfully test repair. Existing FEC data construction methods often follow a static corruption view: they mask spans in false claims or inject errors into supported claims, but do not explicitly target the boundary of what current evidence-based correctors can repair. We introduce the notion of a \emph{repair frontier}: factual errors that are evidence-grounded, close enough to the source claim to admit a clear correction, and resistant to current correctors. We propose \textsc{SAFE-FEC}, a semantically constrained adversarial frontier-evolution framework for constructing such examples. Starting from evidence-supported claims, SAFE-FEC generates mutations from multiple factual-error perspectives, refines them through cross-perspective critique, selects or composes stronger candidates, filters semantic drift, and accepts only candidates that survive repeated corrector-in-the-loop repair attempts. This turns FEC data construction from one-shot error injection into correction-aware frontier search. Experiments on FECData, HoVer, and FEVEROUS show that SAFE-FEC consistently lowers correction performance for both LLM-based and FEC-specific correctors, especially in multi-hop and structured-evidence settings. Quality evaluation and ablations further suggest that these failures arise from plausible, semantically anchored, evidence-grounded errors rather than invalid hard negatives.