Matrix-Game 4.0: Harnessing Video Models for Real-Time Playable Game Worlds
Introduction A game world built by a video generation model alone stores off-screen state nowhere. Every time a corridor you have already passed through is redrawn, it…
Attention Optimization
14 pages
FlashMask-3: Efficient and Expressive Mask-Aware Distributed Attention
An analysis of FlashMask-3’s Hopper kernels, mask localization, load balancing, communication overlap, and long-context training results and limitations.
MC-Sparse: Deconstructing and Closing the Dense-Sparse Attention Gap in Diffusion Transformers
MC-Sparse separates three sources of quality and efficiency loss in sparse DiT attention. This review explains why token selection and kernel execution costs must be…
Mechanics of Long-Context Hybrid Models Part 1.1: From Hybrid Attention to Hybrid Position
Mechanics of Long-Context Hybrid Models: From Hybrid Attention to Hybrid Position TL;DR — These days open-source LLMs are rapidly shifting from models that "use full…
SlimKV: Joint Token-Feature KV Cache Compression with Reconstruction-Free Beacon Attention
SlimKV: Compressing Tokens and Features Together, Cutting the KV Cache by up to 32× with Reconstruction-Free Latent-Space Attention TL;DR SlimKV is a question-agnostic…
The KV Cache Is the New Memory Wall
The KV Cache Is the New Memory Wall: Three Regimes and Five Domains of Long-Context LLM Inference TL;DR — Long-context LLM decode inference is bound by memory bandwidth,…
MassAlloc Attention: Let Attention Allocate Its Own Compute
Paper MassAlloc Attention (MALA): Letting Attention Allocate Its Own Compute TL;DR — Full softmax attention (FullAttn) performs all post-score computation after QK score…
WorldAttention: An Efficient Attention Architecture for Interactive Video World Models
WorldAttention: System Co-Design of an Interactive Video World Model That Drops the Sliding Window TL;DR — Text-controlled interactive video world models are trapped…
Block Sparse Attention with Log-Linear Complexity
PISA: Pyramid Top-$K$ Selection Lowers the Cost of Block-Sparse Attention to $O(N\log N)$ TL;DR — Block-sparse attention makes attention computation linear, but block…
Fathom: Per-Query Read Depth for Sparse Decoding over Offloaded KV Caches
Fathom: Accelerating 1M-Token Decoding by 1.67x with Query-Adaptive Depth Scanning TL;DR When agent sessions stretch to 1M tokens and dozens of sessions reside…
SAS: Simple Attention Sparsification via End-to-End Optimization of Context Ranking
SAS: A Simple Attention Sparsification That Learns Context Ranking Directly Without Distillation TL;DR — In post-training attention sparsification, inject the continuous…
Kalman Delta Networks: Uncertainty-aware Associative Memory
Linear Attention Rewritten with a Kalman Filter: Kalman Delta Networks TL;DR The fixed-size recurrent memory update of linear attention is reinterpreted as a…
![[Paper Review] Hardware-Efficient Attention for Fast Decoding](https://moonlight-paper-snapshot.s3.ap-northeast-2.amazonaws.com/arxiv/hardware-efficient-attention-for-fast-decoding-1.png)
[Paper Review] Hardware-Efficient Attention for Fast Decoding
GTA & GLA: Hardware-Efficient Attention That Breaks the 'Memory-Dominated' Decode TL;DR GTA (key–value tying) and GLA (latent head sharding) raise FLOPs/byte and shrink…
![[Paper Review] Llama-Nemotron: Efficient Reasoning Models](https://cdn-thumbnails.huggingface.co/social-thumbnails/collections/nvidia/llama-nemotron-67d92346030a2691293f200b.png)
[Paper Review] Llama-Nemotron: Efficient Reasoning Models
Hydragen: The Secret Weapon for Decoding Large Batches with Shared Prefixes up to 32× Faster TL;DR By decomposing the prefix and suffix using softmax denominator…