405rar

: 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.

: 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

RAR is an autoregressive (AR) image generator designed to be fully compatible with standard language modeling frameworks. It aims to bridge the gap between traditional AR models and more flexible bidirectional models like diffusion or masked transformers. : It introduces a randomness annealing strategy with

: A suite released in April 2024 to evaluate how well retrieval models can perform reasoning tasks typically reserved for Large Language Models (LLMs). AI responses may include mistakes