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This paper proposes Time–Frequency Geometric Cross-Attention (TFGCA), a drop-in module for chunked vision-language-action models that improves action trajectory prediction by decomposing chunks into time-frequency representations and capturing geometric relationships, resulting in significant performance gains on benchmarks and real-robot tasks.
Introduces CamoNAS, a frequency-aware multi-resolution Neural Architecture Search framework for camouflaged object detection, achieving state-of-the-art results on four benchmarks.
CogSENet introduces a blind image deblurring framework inspired by eagle vision, using semantic-aware modules and frequency decomposition to improve restoration quality and structural fidelity, outperforming state-of-the-art methods.
FlowLet is a conditional generative framework that synthesizes age-conditioned 3D brain MRIs using flow matching in an invertible wavelet domain, improving brain age prediction accuracy for underrepresented age groups with high efficiency.
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.
WaveFilter proposes a training-free, wavelet-guided KV cache filtering framework for diffusion large language models that enhances long-context capability by precisely identifying key tokens and constructing sparse caches, improving performance on complex long-context tasks.
This paper proposes DSFM, a novel generative framework that uses wavelet decomposition and spectral flow matching to synthesize realistic fMRI time series for brain disorder identification, addressing data scarcity and non-stationarity challenges.
Introduces QuChaTeR, a hybrid architecture combining wavelet-based preprocessing, chaotic maps, and variational quantum circuits with recurrent structures for earthquake prediction, demonstrating faster convergence and superior accuracy over classical and quantum baselines.