Constrained latent state modeling: A unifying perspective on representation learning under competing constraints

תקציר מקורי באנגליתarXiv:2605.15995v2 Announce Type: replace-cross Abstract: Learning latent representations from complex data is central to modern machine learning, spanning temporal, multimodal, and partially observed systems. In such settings, representations are more naturally understood as latent states capturing underlying system dynamics rather than compressed summaries of observations. Yet current approaches remain fragmented, relying on distinct, often implicit, assumptions about what these states should represent. We argue that this fragmentation reflects a more fundamental limitation: latent representations are typically learned from underconstrained objectives that fail to specify the properties that meaningful latent states should satisfy. As a result, multiple representations may satisfy the sa