RoPE frequency usage is determined by a data-induced dependency width W, with the optimal frequency scaling as π/W, explaining both learned spectra and the success of position interpolation.
Efficient streaming language models with attention sinks
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
TriAxialKV introduces triaxial mixed-precision KV-cache quantization that matches BF16 accuracy at 4.5x cache size and 30% higher throughput for a Qwen3-VL agent on OSWorld.
Zero-ablation overstates register content dependence in DINO ViTs because mean, noise, and cross-image shuffle replacements preserve performance while zeroing does not.
GHOST is a geometry-hierarchical token eviction framework that halves the KV cache size in monocular video 3D reconstruction while maintaining quality and achieving 1.75x faster inference.
Self Forcing trains autoregressive video diffusion models by performing autoregressive rollout with KV caching during training to close the exposure bias gap, using a holistic video-level loss and few-step diffusion for efficiency.
HorizonStream is a long-horizon Transformer that factorizes geometric evidence influence into channel-wise linear attention for long-range temporal propagation and local spatiotemporal attention for short-range matching, claiming stable generalization from 48-frame training to over 10,000-frame test
citing papers explorer
-
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization
RoPE frequency usage is determined by a data-induced dependency width W, with the optimal frequency scaling as π/W, explaining both learned spectra and the success of position interpolation.
-
TriAxialKV: Toward Extreme Low-Precision KV-Cache Quantization for Agentic Inference Tasks
TriAxialKV introduces triaxial mixed-precision KV-cache quantization that matches BF16 accuracy at 4.5x cache size and 30% higher throughput for a Qwen3-VL agent on OSWorld.
-
Zero-Ablation Overstates Register Content Dependence in DINO Vision Transformers
Zero-ablation overstates register content dependence in DINO ViTs because mean, noise, and cross-image shuffle replacements preserve performance while zeroing does not.
-
GHOST: Geometry-Hierarchical Online Streaming Token Eviction for Efficient 3D Reconstruction
GHOST is a geometry-hierarchical token eviction framework that halves the KV cache size in monocular video 3D reconstruction while maintaining quality and achieving 1.75x faster inference.
-
Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion
Self Forcing trains autoregressive video diffusion models by performing autoregressive rollout with KV caching during training to close the exposure bias gap, using a holistic video-level loss and few-step diffusion for efficiency.
-
HorizonStream: Long-Horizon Attention for Streaming 3D Reconstruction
HorizonStream is a long-horizon Transformer that factorizes geometric evidence influence into channel-wise linear attention for long-range temporal propagation and local spatiotemporal attention for short-range matching, claiming stable generalization from 48-frame training to over 10,000-frame test