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#autoregressive-models News & Analysis

7 articles tagged with #autoregressive-models. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

7 articles
AIBearisharXiv – CS AI · 6d ago7/10
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On the Robustness of Watermarking for Autoregressive Image Generation

Researchers demonstrate critical vulnerabilities in watermarking techniques designed for autoregressive image generators, showing that watermarks can be removed or forged with access to only a single watermarked image and no knowledge of model secrets. These findings undermine the reliability of watermarking as a defense against synthetic content in training datasets and enable attackers to manipulate authentic images to falsely appear as AI-generated content.

AIBullisharXiv – CS AI · Mar 97/10
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CanvasMAR: Improving Masked Autoregressive Video Prediction With Canvas

Researchers have developed CanvasMAR, a new masked autoregressive video prediction model that generates high-quality videos with fewer sampling steps by using a "canvas" approach that provides global structure early in the generation process. The model demonstrates superior performance on major benchmarks including BAIR, UCF-101, and Kinetics-600, rivaling advanced diffusion-based methods.

AIBullisharXiv – CS AI · Mar 46/102
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Chain of World: World Model Thinking in Latent Motion

Researchers introduce CoWVLA (Chain-of-World VLA), a new Vision-Language-Action model paradigm that combines world-model temporal reasoning with latent motion representation for embodied AI. The approach outperforms existing methods in robotic simulation benchmarks while maintaining computational efficiency through a unified autoregressive decoder that models both keyframes and action sequences.

AINeutralarXiv – CS AI · 6d ago6/10
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Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow

Researchers evaluated eight large Masked Diffusion Language Models (up to 100B parameters) and found they still underperform comparable autoregressive models despite promises of parallel token generation. The study reveals MDLMs exhibit task-dependent decoding behavior and propose a Generate-then-Edit paradigm to improve performance while maintaining parallel processing efficiency.

AIBullisharXiv – CS AI · Apr 136/10
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WAND: Windowed Attention and Knowledge Distillation for Efficient Autoregressive Text-to-Speech Models

Researchers introduce WAND, a framework that reduces computational and memory costs of autoregressive text-to-speech models by replacing full self-attention with windowed attention combined with knowledge distillation. The approach achieves up to 66.2% KV cache memory reduction while maintaining speech quality, addressing a critical scalability bottleneck in modern AR-TTS systems.

AIBullisharXiv – CS AI · Apr 136/10
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AR-KAN: Autoregressive-Weight-Enhanced Kolmogorov-Arnold Network for Time Series Forecasting

Researchers propose AR-KAN, a neural network combining autoregressive models with Kolmogorov-Arnold Networks for improved time series forecasting. The model addresses limitations of traditional deep learning approaches by integrating temporal memory preservation with nonlinear function approximation, demonstrating superior performance on both synthetic and real-world datasets.