arXiv:2607. 23514v1 Announce Type: cross Abstract: Multimodal automated fact-checking (MAFC) verifies claims by retrieving and reasoning over external evidence.
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Multimodal automated fact-checking (MAFC) verifies claims by retrieving and reasoning over external evidence. However, most existing static benchmarks risk contamination: they primarily consist of outdated claims verifiable using an LLM's internal knowledge without external evidence.
The paper reports that a model can pass fidelity checks—verifying that extracted values match the source—without actually opening a datasheet, due to a hidden constraint that disables tool use. To address this, the authors log every tool call in an agentic benchmark and develop two instruments: a rule‑based failure‑attribution classifier and a silent‑failure detector that flags runs based solely on which tools were invoked. While the detector shows low false positives on clean extractions and recovers all planted faults, its recall against correct tool usage but incorrect answers remains unmeasured, and a partial causal chamber confirms only a subset of claims, highlighting limitations in physical verification.
arXiv:2608. 03372v1 Announce Type: cross Abstract: AI systems rewrite information constantly: conversations become stored memories, documents become answers.
The paper evaluates the robustness of automated fact‑checking systems by cross‑benchmarking nine models—including random baselines, fine‑tuned transformers, zero‑shot LLMs, and top AVeriTeC 2025 systems—across four datasets from scientific, open‑web, and climate domains. It finds that fine‑tuned models outperform zero‑shot LLMs on ClimateCheck, that system rankings vary strongly with domain and metric, and that replacing retrieved evidence with gold annotations boosts veracity accuracy by 14–22 points, underscoring retrieval as the main bottleneck. The authors provide code, pre‑processed datasets, and results to enable reproducible research.
Automated fact-checking (AFC) systems retrieve evidence and predict claim veracity, yet evaluations omit simple baselines, systems are developed for a single benchmark and cannot be trusted to general...
arXiv:2608. 12652v1 Announce Type: cross Abstract: Benchmark contamination is diagnosed today with n-gram overlap, with likelihood-based membership inference, or with canary strings, and each needs something usually unavailable: the training corpus, a well-chosen test statistic, or foresight at dataset release.