This paper introduces MARGIN (Multi-Agent Runtime Grading via Incremental Normalisation), a runtime calibration method that corrects model confidence based on their responses without retraining or a separate calibration set. The method uses the ratio of accuracy and stated confidence to adjust scores, improving the accuracy of answer selection in collective decision-making.
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MARGIN can be useful for improving the accuracy of coordination among heterogeneous models when correctness feedback is available.
NAMVIS is a diffusion-free framework for multi-view image synthesis that uses geometry-conditioned next-scale autoregression, predicting discrete visual tokens through coarse-to-fine scale steps and parallel sampling across target views. It outperforms diffusion-based methods in PSNR, SSIM, and LPIPS while being over three times faster.
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