A control-theoretic linear program yields value-driven transport policies for generative modeling with straight paths and simulation-free training.
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ReKep encodes robotic tasks as optimizable Python functions over 3D keypoints that are generated automatically from language and RGB-D input, enabling real-time hierarchical planning on single- and dual-arm platforms without task-specific data.
KNN-distance outlier removal yields a constant-factor reduction from robust k-means to standard k-means under cluster-size assumptions.
TSPG applies conditional GANs to generate realistic transcriptome perturbations that mimic source-to-target gene expression state transitions and highlight biologically enriched genes.
URSA is a modular agent ecosystem that uses LLMs and scientific tools to accelerate research tasks of varying complexity.
citing papers explorer
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Generative Modeling by Value-Driven Transport
A control-theoretic linear program yields value-driven transport policies for generative modeling with straight paths and simulation-free training.
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ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation
ReKep encodes robotic tasks as optimizable Python functions over 3D keypoints that are generated automatically from language and RGB-D input, enabling real-time hierarchical planning on single- and dual-arm platforms without task-specific data.
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Simple KNN-Based Outlier Detection Achieves Robust Clustering
KNN-distance outlier removal yields a constant-factor reduction from robust k-means to standard k-means under cluster-size assumptions.
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Cellular State Transformations using Generative Adversarial Networks
TSPG applies conditional GANs to generate realistic transcriptome perturbations that mimic source-to-target gene expression state transitions and highlight biologically enriched genes.
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URSA: The Universal Research and Scientific Agent
URSA is a modular agent ecosystem that uses LLMs and scientific tools to accelerate research tasks of varying complexity.