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Introduces RosettaSim, a framework that uses structured autoregressive modeling for long-term traffic simulation, achieving state-of-the-art performance on the Waymo Open Sim Agent Challenge. Also proposes Retrieval-based Traffic Evaluation (RTE) for better long-horizon fidelity assessment.
DREAM trains dense retrieval embeddings by using autoregressive language model attention to supervise query-document similarity, eliminating the need for labeled data. It consistently outperforms baselines on BEIR and RTEB benchmarks across model scales.
Discrete autoregressive MRI reconstruction using privileged information distillation achieves superior performance under extreme undersampling by leveraging visual autoregressive modeling techniques.