UCGP is a universal physical adversarial patch that compromises cross-modal semantic alignment in IR-VLMs through curved-grid parameterization and representation-space disruption.
Completely derandomized self-adaptation in evolution strategies
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Wiggle and Go! uses system identification from rope motion observations to predict parameters that enable zero-shot goal-conditioned dynamic manipulation, achieving 3.55 cm accuracy on 3D target striking versus 15.34 cm without parameter information.
CCV-QAOA is a new complex-valued continuous-variable variant of QAOA that solves real and complex multivariate optimization problems via a variational framework.
PhDLspec combines differential spectra from physical stellar models with a transformer to derive approximately 30 stellar parameters from low-resolution spectra hundreds of times faster than traditional calculations.
CovAngelo implements a QM/QM/MM embedding model using quantum-information metrics to compute reaction energy profiles and barriers for covalent drug binding at lower cost than conventional methods, demonstrated on zanubrutinib to BTK.
citing papers explorer
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Revealing Physical-World Semantic Vulnerabilities: Universal Adversarial Patches for Infrared Vision-Language Models
UCGP is a universal physical adversarial patch that compromises cross-modal semantic alignment in IR-VLMs through curved-grid parameterization and representation-space disruption.
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Wiggle and Go! System Identification for Zero-Shot Dynamic Rope Manipulation
Wiggle and Go! uses system identification from rope motion observations to predict parameters that enable zero-shot goal-conditioned dynamic manipulation, achieving 3.55 cm accuracy on 3D target striking versus 15.34 cm without parameter information.
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A Complex-Valued Continuous-Variable Quantum Approximation Optimization Algorithm (CCV-QAOA)
CCV-QAOA is a new complex-valued continuous-variable variant of QAOA that solves real and complex multivariate optimization problems via a variational framework.
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PhDLspec: physical-prior embedded deep learning method for spectroscopic determination of stellar labels in high-dimensional parameter space
PhDLspec combines differential spectra from physical stellar models with a transformer to derive approximately 30 stellar parameters from low-resolution spectra hundreds of times faster than traditional calculations.
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CovAngelo: A hybrid quantum-classical computing platform for accurate and scalable drug discovery
CovAngelo implements a QM/QM/MM embedding model using quantum-information metrics to compute reaction energy profiles and barriers for covalent drug binding at lower cost than conventional methods, demonstrated on zanubrutinib to BTK.