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The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 133
Data-Driven Vision-Language Models for Remote Sensing: A Survey
During the deep learning era, innovations in remote sensing (RS) vision models primarily focused on optimizing network architectures for specific tasks and conducting end-to-end training.The Bayesian Learning Rule Beyond KL Geometry
The Bayesian learning rule (BLR) exploits the relation between Bayesian updating and the Kullback-Leibler divergence (KLD) to derive efficient additive updates for exponential family variational…Extreme Compression of Per-Layer Embeddings via Cross-Layer Latent Vector Quantization
Scaling embedding capacity alongside Transformer computation is an emerging trend in language models.Identifiability from Stationary Covariance in Linear SDEs with Reciprocal Interactions and Latent Variables
Continuous-time stochastic differential equations (SDEs) provide a natural framework for modeling interacting dynamical systems.The Value of System 1 Models in Medicine
Many clinical decisions are a choice among a few fixed options, taken quickly and in large numbers.Bridging the Data Gap: Source-Informed Task-Space Transport for Few-Shot Diffusion
Many deployments require a model to move rapidly from a data-rich source to a related target observed only sparsely.AgenticEvolve: Evolutionary Program Search with Persistent Language Model Agents that Design and Run Experiments
Evolutionary program search with large language models keeps the structure of an evolutionary algorithm and uses the model as its variation operator.Deep Learning for Random Objects in Metric Spaces: Beyond Manifold Assumptions
Deep learning (DL) methods have substantially improved predictive performance across a wide range of downstream prediction tasks, enabling new discoveries in multiple domains.Harmful Responses Are Jailbreak Templates: Transferring Autoregressive Jailbreaks to Diffusion Language Models
Diffusion large language models (dLLMs) enable flexible masked generation through bidirectional context modeling, but this capability also introduces a distinct jailbreak attack surface.Metric Conformal Prediction via the Conditional Expected Local Radius
We introduce the conditional expected local radius (CELR) and develop a new conformal prediction method for non-Euclidean responses in general metric spaces.Graph-Recurrent Learning of Emergent Collective Dynamics
Collective ordering in correlated electron systems emerges from microscopic dynamics, yet the evolution of observable textures need not admit a closed, memoryless description.AfterDeleteBench: Auditing Post-Deletion Information Persistence in LLM Agents
Many LLM agents maintain external persistent memory and derive additional state from past interactions, such as summaries.Convex Equilibrium Potentials for Monge--Ampère: Curvature Bounds and A Posteriori Evaluation
Equilibrium neural networks define their hidden state as the fixed point of a contraction, so their effective depth is set at evaluation time rather than by the architecture.HERD: Harness-Evolving Recursive Distillation for Continual Learning
Continual learning requires a model to keep acquiring new skills and knowledge without compromising its existing capabilities.SQuAT: Spherical Lattice Vector Quantization-Aware Training
Vector quantization (VQ), in particular lattice-based methods, offer strong rate-distortion trade-offs for low-bit large language models (LLMs), yet their behavior under quantization-aware training…Post-Hoc Feature Selection Layer for Neural Networks Interpretability
The interpretability of complex neural networks remains a critical challenge, especially for models already deployed in high-stakes domains.How Depth Growth and Looping Shape In-Context Learning in Language Models
Looped and depth-grown language models reuse computations across depth and have attracted interest for their reasoning capabilities.Predictable performance is suboptimal, and what it means for model routing
Large language models (LLMs) differ in capabilities and costs, motivating routing methods that adaptively choose a model for each query.Dimension-free regimes for unadjusted Langevin in ridge-separable models: A subspace robust Wasserstein analysis
The Unadjusted Langevin Algorithm (ULA) is widely used for high-dimensional Bayesian inference.Drift Says When, Budget Says How Much: Rate-Controlled KV Re-selection for Long-Form Multimodal Generation
KV cache compression for multimodal LLMs usually selects the visual tokens to keep once, at prefill, and reuses that selection for the whole generation.