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Noise-Aware Verbal Confidence CAlibration Rules are proposed to provide a principled foundation for resolving overconfidence under noise and NOVA, a noise-aware calibration framework that synthesizes supervision from ~2K HotpotQA examples guided by these rules.
A cross-modal grounding study shows that text-level VCI ordering largely survives faithful rendering and blind image-level preference judgment, supporting Ekphrasis as a measure of visual ideation beyond prose quality.
This work proposes InferenceDynamics, a flexible and scalable multi-dimensional routing framework by modeling the capability and knowledge of models, and demonstrates its effectiveness and generalizability in group-level routing using modern benchmarks including MMLU-Pro, GPQA, BigGen-Bench, and LiveBench.
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