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This paper presents a transferable learned temporal prior for outbreak transmission reconstruction, demonstrating improved performance on a real Andes virus benchmark and highlighting the importance of quantifying uncertainty in transmission labels.
This paper proposes a reinforcement learning approach to enable large language models to translate unseen languages by leveraging in-context linguistic knowledge, outperforming in-context learning and supervised fine-tuning.
ReactiveGWM is a reactive game world model that enables dynamic player-NPC interactions by decoupling player controls from NPC behaviors using diffusion models and cross-attention modules, achieving zero-shot strategy transfer across different games.
The paper introduces Diamond Attention, a method for multi-agent reinforcement learning that uses structured randomness to break symmetry and enable role differentiation among homogeneous agents, achieving perfect coordination in symmetric tasks like the XOR game.
CPCANet is a domain generalization framework that uses Common Principal Component Analysis to discover structured domain-invariant subspaces, achieving state-of-the-art performance in zero-shot transfer.