Dreamer 4 is the first agent to obtain diamonds in Minecraft from only offline data by reinforcement learning inside a scalable world model that accurately predicts game mechanics.
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Axial attention in multidimensional transformers
11 Pith papers cite this work. Polarity classification is still indexing.
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Medusa augments LLMs with multiple decoding heads and tree-based attention to predict and verify several tokens in parallel, yielding 2.2-3.6x inference speedup via two fine-tuning regimes.
A diffusion model for video generation extends image architectures with joint image-video training and improved conditional sampling, delivering first large-scale text-to-video results and state-of-the-art performance on video prediction and unconditional generation benchmarks.
VECA learns effective visual representations using core-periphery attention where patches interact exclusively via a resolution-invariant set of learned core embeddings, achieving linear O(N) complexity while maintaining competitive performance.
Transformer components arise as the natural solution to precision-weighted directional state estimation on the hypersphere.
ESFM is a single open foundation model that unifies heterogeneous Earth data sources and forecasts missing regions while preserving inter-variable physical relationships.
MoBA routes attention over blocks via MoE-style gating to enable dynamic, bias-light long-context attention that matches full attention performance at lower cost.
YOLOv12 is a new attention-based real-time object detector that reports higher accuracy than YOLOv10, YOLOv11, and RT-DETR variants at comparable or better speed and efficiency.
Deformable DETR achieves higher accuracy than DETR, especially on small objects, while converging in one-tenth the training epochs by using sparse deformable attention on image features.
Jukebox generates high-fidelity and diverse songs with singing and coherence up to multiple minutes by compressing raw audio via multi-scale VQ-VAE and modeling the codes with large autoregressive Transformers conditioned on artist, genre, and unaligned lyrics.
A multi-block attention neural network reduces pilot overhead by 87% and NMSE by 51% at 10 dB SNR for cascaded channel estimation in IRS-assisted mmWave MIMO-OFDM systems.
citing papers explorer
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Training Agents Inside of Scalable World Models
Dreamer 4 is the first agent to obtain diamonds in Minecraft from only offline data by reinforcement learning inside a scalable world model that accurately predicts game mechanics.
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Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads
Medusa augments LLMs with multiple decoding heads and tree-based attention to predict and verify several tokens in parallel, yielding 2.2-3.6x inference speedup via two fine-tuning regimes.
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Video Diffusion Models
A diffusion model for video generation extends image architectures with joint image-video training and improved conditional sampling, delivering first large-scale text-to-video results and state-of-the-art performance on video prediction and unconditional generation benchmarks.
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Elastic Attention Cores for Scalable Vision Transformers
VECA learns effective visual representations using core-periphery attention where patches interact exclusively via a resolution-invariant set of learned core embeddings, achieving linear O(N) complexity while maintaining competitive performance.
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RT-Transformer: The Transformer Block as a Spherical State Estimator
Transformer components arise as the natural solution to precision-weighted directional state estimation on the hypersphere.
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Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting
ESFM is a single open foundation model that unifies heterogeneous Earth data sources and forecasts missing regions while preserving inter-variable physical relationships.
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MoBA: Mixture of Block Attention for Long-Context LLMs
MoBA routes attention over blocks via MoE-style gating to enable dynamic, bias-light long-context attention that matches full attention performance at lower cost.
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YOLOv12: Attention-Centric Real-Time Object Detectors
YOLOv12 is a new attention-based real-time object detector that reports higher accuracy than YOLOv10, YOLOv11, and RT-DETR variants at comparable or better speed and efficiency.
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Deformable DETR: Deformable Transformers for End-to-End Object Detection
Deformable DETR achieves higher accuracy than DETR, especially on small objects, while converging in one-tenth the training epochs by using sparse deformable attention on image features.
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Jukebox: A Generative Model for Music
Jukebox generates high-fidelity and diverse songs with singing and coherence up to multiple minutes by compressing raw audio via multi-scale VQ-VAE and modeling the codes with large autoregressive Transformers conditioned on artist, genre, and unaligned lyrics.
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Multi-Block Attention for Efficient Channel Estimation in IRS-Assisted mmWave MIMO
A multi-block attention neural network reduces pilot overhead by 87% and NMSE by 51% at 10 dB SNR for cascaded channel estimation in IRS-assisted mmWave MIMO-OFDM systems.