Tag
Introduces Semantic Compression Trees (SCT) for hierarchical knowledge retrieval in RAG, demonstrating efficiency gains in token usage but mixed retrieval performance compared to flat methods.
The paper proposes MR-Traj, a multi-resolution diffusion framework for synthetic trajectory generation in urban systems, which captures complex spatial-temporal dependencies at multiple resolutions and improves performance in fine-grained mobility modeling for downstream tasks.
This paper introduces HP-JEPA, a hierarchical partitioning framework for multi-resolution graph joint-embedding predictive learning, which outperforms the fixed-resolution Graph-JEPA baseline on most graph classification and regression benchmarks.
MrFlow is a training-free multi-resolution acceleration strategy for flow-matching text-to-image models that combines low-resolution generation with pixel-space super-resolution and noise injection, achieving up to 25x end-to-end speedup without training or runtime modifications.
Proposes TempoWave, a plug-and-play temporal wavelet digit interface that maps time series observations into digit-wise embeddings from multi-wavelet coefficients, improving LLM-based time series forecasting and achieving state-of-the-art on multiple benchmarks.
SKIM is an adaptive multi-resolution soft token compression framework that compresses procedural skills for LLMs, maintaining task performance while reducing prefill cost and latency.
WaveScope is an MCP server that applies wavelet transforms to codebases, providing LLMs with multi-resolution structural context to improve code understanding and editing, addressing context rot and structural awareness.
The paper introduces beignet, a PINN architecture that replaces random Fourier features with a trainable multi-resolution Fourier feature pyramid, achieving higher accuracy and computational efficiency on PDE benchmarks.