AI RESEARCH
Is the reconstruction loss culprit? An attempt to outperform JEPA
arXiv CS.LG
•
ArXi:2603.14131v1 Announce Type: new We evaluate JEPA-style predictive representation learning versus reconstruction-based autoencoders on a controlled "TV-series" linear dynamical system with known latent state and a single noise parameter. While an initial comparison suggests JEPA is markedly robust to noise, further diagnostics show that autoencoder failures are strongly influenced by asymmetries in objectives and by bottleneck/component-selection effects (confirmed by PCA baselines). Motivated by these findings, we.