AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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INCONVENIENT is introduced, a benchmark designed to study robustness to convenience-sample bias and provides explicit control over both the direction and severity of relationship shifts, enabling a systematic assessment of robustness to convenience sampling.
Deployed vision-language systems often gate their answers on confidence, making confidence robustness relevant to oversight. We study confidence readouts under white-box, image-only attacks constrained to preserve the generated answer byte-identically. Under a reachability assumption, an unmovable readout cannot outper...
This work evaluates seven confidence estimators, three inference-only and four trained internal probes, across five open-weight LVLMs and four conditions from three financial visual question-answering benchmarks, one bilingual; every probe is trained only on natural images and applied to finance without adaptation, so...
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