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: It introduces a randomness annealing strategy with a permuted objective . This allows the model to learn bidirectional contexts—seeing different parts of the image simultaneously—without needing extra computational costs or changing the basic autoregressive structure.

: The paper and its associated codebase are available through platforms like arXiv and GitHub . Related Benchmarks & Agents 405rar

: A suite released in April 2024 to evaluate how well retrieval models can perform reasoning tasks typically reserved for Large Language Models (LLMs). : It introduces a randomness annealing strategy with

: A framework proposed in early 2026 that uses "Rationale-Augmented Retrieval" to reduce hallucinations and improve query formulation in AI agents. AI responses may include mistakes. Learn more [2411.00776] Randomized Autoregressive Visual Generation 405rar