Tag
Proposes Asymmetric Mutual Variational Learning (AMVL) to resolve train-inference mismatch in multimodal continuous reasoning by using bidirectional calibration to prevent answer leakage and improve latent-space stability, achieving significant gains on the BLINK benchmark.
This paper introduces Data-Driven Variational Basis Learning (DVBL), a non-neural framework that learns basis functions directly from data through variational optimization, offering interpretability and mathematical transparency compared to neural networks.