Sparse attention is used to reduce latency in long-sequence generation, but existing methods can degrade generation quality at high sparsity. MC-Sparse is a training-free framework that selects individual key-value tokens and organizes similar queries into groups for efficient GPU execution.
The chart will appear after repeat observations. The current metric comes from the source.
16 source votesReal observations only. History before source connection is not reconstructed.
May be useful for accelerating video and 3D model generation without quality loss.
The paper introduces Trace2Env, a learning-free framework that uses historical interaction traces to create a reusable environment worldbook for simulating realistic environments without rebuilding the original system. It improves next-observation fidelity and long-horizon interaction consistency compared to conventional prompt-based language world models.
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3 days agoDaily PapersTetris3D is a generative framework for 3D scene reconstruction from a single image, ensuring objects are geometrically and physically coherent. It uses explicit conditioning on surrounding objects' geometry and physical relationships, along with a physics-based dataset called ComOb for training and evaluation.
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3 days agoDaily PapersNAMVIS is a diffusion-free framework for multi-view image synthesis that uses geometry-conditioned next-scale autoregression, predicting discrete visual tokens through coarse-to-fine scale steps and parallel sampling across target views. It outperforms diffusion-based methods in PSNR, SSIM, and LPIPS while being over three times faster.
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2 days ago