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This paper addresses the problem of poor out-of-distribution (OOD) language instruction generalization in Vision-Language-Action (VLA) models, particularly those using continuous action experts tra…
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This paper addresses the challenge of shrinkage estimation for spatially structured parameters when multiple plausible definitions of relatedness among units exist (e.g., geography, adjacency, or c…
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Atlas H&E-TME addresses the challenge of scalable, quantitative tissue profiling from routine hematoxylin and eosin (H&E) whole-slide images (WSIs), which are widely but qualitatively used in patho…
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CHORUS addresses the challenge of decentralized multi-robot collaboration without requiring joint observations or inter-robot communication at inference time
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This paper presents a comprehensive weak-lensing (WL) and multiwavelength analysis of the nearby, rich Coma galaxy cluster (Abell 1656, z=0.023), aiming to characterize its dark matter distribution…
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This work addresses the unsolved challenge of achieving generalized metric monocular depth estimation for both narrow field-of-view (FoV) perspective images and 360° panoramic images within a unifi…
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Doc-to-Atom (Doc2Atom) addresses the inefficiencies and limitations of prior document internalization approaches like Doc-to-LoRA, which compress an entire document into a single monolithic low-ran…
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This paper addresses the challenge of ensuring representative exposure to the full range of arguments in large-scale deliberative polling, known as the coverage problem
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FitVTON addresses a critical gap in virtual try-on (VTON) research where current diffusion-based methods achieve impressive visual realism but fail to produce physically authentic garment fit acros…
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This paper addresses a key limitation in robotic imitation learning policies conditioned on 3D point cloud observations
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This paper addresses the challenge of accurately inferring the physical properties—age and mass—of star clusters in the dwarf starburst galaxy NGC 1569, which is located about 3.25 Mpc away and und…
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This paper addresses the growing need to identify AI-generated code within modern software codebases, which increasingly contain a mix of human- and AI-authored code at fine granularity rather than…
