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cs.LG2026
Qantara: Bridge-Flow Training for Multi-Paradigm JEPA Inference
Ruslan Rakhimov, George Bredis, Yuriy Maksyuta +1
Joint-Embedding Predictive Architectures (JEPAs) underpin a growing family of latent world models for control from raw pixels. Published JEPA world models for pixel-based control (…
cs.LG2026
Rank-Then-Act: Reward-Free Control from Frame-Order Progress
Yuriy Maksyuta, George Bredis, Ruslan Rakhimov +1
We introduce Rank-Then-Act (RTA), a framework for learning control policies from expert video demonstrations without environment rewards. RTA trains a Vision-Language Model (VLM) o…
cs.LG2026
Next Embedding Prediction Makes World Models Stronger
George Bredis, Nikita Balagansky, Daniil Gavrilov +1
Capturing temporal dependencies is critical for model-based reinforcement learning (MBRL) in partially observable, high-dimensional domains. We introduce NE-Dreamer, a decoder-free…