CUSP: Decomposable Collective Uncertainty for Multi-Agent Multimodal Reasoning
Read the original on arXiv AI →CUSP (Collective Uncertainty through Semantic Opinion Pooling) is a training‑free framework that aggregates responses from multiple vision‑language models into a shared semantic space, producing a pooled opinion and two system‑level uncertainty signals: collective uncertainty (dispersion) and Jensen‑Shannon divergence (model conflict). It decomposes collective entropy into the mean of individual semantic entropies plus JSD, enabling reliable uncertainty estimation without token logits or calibration labels. In static ensembles, collective uncertainty outperforms baseline methods for error detection and abstention, while JSD excels in commercial settings, and the pooled prediction consistently improves accuracy over individual models. "whyItMatters":"CUSP provides a practical, model‑agnostic way to quantify system‑level reliability and improve decision‑making in multimodal reasoning tasks."
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