arXiv:2605.28693v1 Announce Type: new Abstract: Backpropagation is the core learning mechanism underlying deep learning. However, whether and how this algorithm is implemented in the brain remains highly debated. In particular, while forward activations of pretrained models reliably map onto the cortical hierarchy of visual processing, it is unknown whether backpropagated gradients exhibit a similar correspondence. […]
Vision-OPD: Learning to See Fine Details for Multimodal LLMs via On-Policy Self-Distillation
arXiv:2605.18740v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) still struggle with fine-grained visual understanding, where answers often depend on small but decisive evidence in the full image. We observe a regional-to-global perception gap: the same MLLM answers fine-grained questions more accurately when conditioned on evidence-centered crops than on the corresponding full images, suggesting […]
Identifying Explicit Parsimonious Piece-wise Polynomial Relationships in Industrial time-series: Application to manipulator robots
arXiv:2605.28320v1 Announce Type: cross Abstract: This paper addresses the problem of identifying parsimonious explicit piece-wise polynomial relationships that might involve a relatively large number of raw features. The algorithm leverages a recently proposed identification algorithm that yields parsimonious implicit relationships enabling to derive normality characterization in the context of anomaly detection and localization. The algorithm […]
Experimental Collapse in Virophysics: Protocol-Resolved Observation, Inference, and Plaque-Assay Blindness
arXiv:2605.27928v1 Announce Type: cross Abstract: Virological measurements are often treated as reports of virion structure, mechanics, dielectric response, infectivity, or titer. In practice, an experiment observes a protocol-conditioned projection of a richer latent virion–environment ensemble. This paper defines this process as emphexperimental collapse within protocol-resolved virophysics. Its central object is the null-inclusive observation operator (P_mathrmobs,t^varnothing(cdotmid […]
Informing AI Policy Assessment using Large-Scale Simulation of Interventions
arXiv:2605.27395v1 Announce Type: cross Abstract: As the rapid proliferation of AI systems and harms spurs efforts in AI governance around the world, prioritizing among competing policy options has become increasingly challenging for policymakers and researchers. We introduce a methodology for identifying viable policy options to mitigate specified AI harms, helping policymakers and researchers target areas […]
Learning Theory of the SVRG: Generalization and Convergence Analysis
arXiv:2605.28513v1 Announce Type: cross Abstract: Variance reduction (VR) methods employ stochastic gradients with decreasing variance, and they have been widely applied to solve large-scale optimization problems in machine learning because of their efficiency. Existing theoretical studies of VR methods are mainly focused on the convergence analysis, leaving the generalization behavior largely unexplored. In this paper, […]
Real-Time In Silico Modeling of Postprandial Macronutrient Kinetics: A Validated Computational Engine for Nutrition Research and Digital Health
arXiv:2605.27459v1 Announce Type: new Abstract: Simulation of post-prandial pharmacokinetics, such as muscle protein synthesis (MPS) through mTORC1 and insulin-induced glucose uptake, is often challenging due to the computational intensity of the multi-compartmental approach. In this study, I introduce an in silico metabolic simulator that uses bi-compartmental Bateman kinetic processes, gamma-variate distributions, and finite state machine […]
Voice “Cloning” is Style Transfer
arXiv:2605.16578v3 Announce Type: replace-cross Abstract: Artificially generated speech is increasingly embedded in everyday life. Voice cloning in particular enables applications where identity preservation is important, such as completing a recording, dubbing in a new language, or preserving the voices of individuals with speech loss. However, in our work, we find that despite the term, voice […]
Heterogeneous Multi-Agent Modeling for Measurement and Network Analysis of the Data Service Market
arXiv:2605.27433v1 Announce Type: cross Abstract: With the increasing complexity of collaboration among various social entities and user demands, the factors affecting the stable development of the data service market are also growing. These factors include the widespread dissemination of information enhancing subjective consciousness, the continuous improvement in intelligence, and the complexification of structural relationships. To […]
Measuring Form and Function in Language Models
arXiv:2605.28616v1 Announce Type: cross Abstract: We introduce quantitative metrics for child language acquisition to evaluate language models. Our focus is on the formal syntactic and functional discourse properties of determiners in English, which young children acquire early and accurately. We propose Contextual Alternative Choice (CAC), a new prompting method which provides targeted tests for both […]
Ligand-Conditioned Discrete Diffusion for Protein Sequence-Structure Co-Design
arXiv:2605.27413v1 Announce Type: new Abstract: Proteins perform their biological functions through three-dimensional structures encoded by amino acid sequences, and ligand-binding protein co-design requires models that generate sequence-structure compatible proteins under explicit ligand constraints. Although continuous diffusion and flow-based models support ligand-aware design in coordinate or latent spaces, existing discrete diffusion protein language models mainly operate […]
Do We Really Need Quantum Machine Learning?: A Multidimensional Empirical Study
arXiv:2605.27923v1 Announce Type: cross Abstract: The rapid growth of computer vision and increasingly complex image recognition tasks has exposed fundamental computational limitations of classical machine learning models, motivating the exploration of quantum computing as an emerging new paradigm. This paper presents a comprehensive benchmarking study of classical and quantum machine learning models for image recognition […]