LLMs discover latent planning strategies up to five steps during training and execute them up to eight steps at test time, with larger models reaching seven under few-shot prompting, revealing a dissociation between discovery and execution.
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Flash-Mono uses a recurrent feed-forward frontend with cross-attention to predict poses and 2D Gaussian surfel attributes for monocular SLAM, achieving 10x speedup and state-of-the-art tracking and mapping.
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The Depth Ceiling: On the Limits of Large Language Models in Discovering Latent Planning
LLMs discover latent planning strategies up to five steps during training and execute them up to eight steps at test time, with larger models reaching seven under few-shot prompting, revealing a dissociation between discovery and execution.
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Flash-Mono: Feed-Forward Accelerated Gaussian Splatting Monocular SLAM
Flash-Mono uses a recurrent feed-forward frontend with cross-attention to predict poses and 2D Gaussian surfel attributes for monocular SLAM, achieving 10x speedup and state-of-the-art tracking and mapping.