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The article presents BFN-RL, a unified generative modeling framework for offline reinforcement learning based on Bayesian Flow Networks, capable of generating effective trajectories across discrete and continuous state spaces.
The paper introduces YB-Mixer, a token-mixing layer derived from the generalized Yang-Baxter equation, which is exactly norm-preserving, depth-stable, and allows order-free and variable-budget inference. It achieves competitive performance on long-range memory tasks with fewer parameters compared to attention and state-space baselines.
Researchers introduce Raven, a novel sequence model that merges state space model efficiency with a selective slot-updating mechanism inspired by sliding window attention to improve long-context retrieval. The approach offers a more principled alternative to existing linear-time models.