In dialogue fact verification (DFV), responses often depend on prior turns for correct interpretation, yet systems are still judged mainly by aggregate benchmark scores. We study a hidden grounding regression: aggregate Macro-F1 improves after fine-tuning while previously correct, context-dependent pronoun cases become newly wrong and show stronger premise-side sensitivity than cases that remain correct. On three referent-annotated audit sets constructed from DialFact and FaithDial, we audit six encoder-only verifiers before and after matched source-specific fine-tuning through prediction-transition analysis and the Premise-Preference Score (PPS), a control-adjusted masking diagnostic. Fine-tuning improves Macro-F1 across the six-model/three-evaluation-set panel, yet newly regressed cases show stronger premise-side sensitivity than stable-correct cases under PPS in 17 of 18 evaluated comparisons, indicating that aggregate gains can conceal regressions on a controlled dialogue-grounding audit.

Fact verification systems rank web evidence by relevance, yet relevant passages may lack verdict-critical facts. We define decision-grade evidence as credible, temporally valid, and claim-matched information independently sufficient to support or refute a claim. Using 750 expert-audited claim–passage pairs for 150 financial claims, we compare relevance and decision-grade supervision within two rerankers while holding candidate evidence pools and verifiers fixed. Within these pools, 66.5% of relevant pairs are insufficient, and claim-level decision-grade coverage trails relevance coverage by 40.0 percentage points. Compared with relevance supervision, decision-grade supervision improves the ranking of sufficient evidence in one reranker but shows no clear advantage in the other. Frozen (untuned) rerankers remain competitive, and downstream verification gains are modest and input-dependent. Thus, relevance is an unreliable proxy for verdict sufficiency; evidence availability, ordering, and verifier use require separate evaluation.

Automated fact-checking in dialogue involves multi-turn conversations where colloquial language is frequent yet understudied. To address this gap, we propose a conservative rewrite candidate for each response claim via staged de-colloquialisation, combining lightweight surface normalisation with scoped in-claim coreference resolution. We then introduce BiCon-Gate, a semantics-aware consistency gate that selects the rewrite candidate only when it is semantically supported by the dialogue context, otherwise falling back to the original claim. On the DialFact benchmark, this gated selection stabilises downstream fact-checking and yields gains in both evidence retrieval and fact verification particularly strong gains on SUPPORTS and outperforms competitive baselines, including a decoder-based one-shot LLM rewrite that attempts to perform all de-colloquialisation steps in a single pass.