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Machine Learning
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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 61
When Everyone Can Build a Model: Mechanistic Inference in the Age of AI
Modern deep learning and AI-assisted code generation are lowering the technical barrier to building mathematical models of biological systems that reproduce complex observations.Improving the Optimization Landscape of Matrix Completion with $ε$-close Surrogates
Non-convex optimization is central to machine learning and AI, yet unlike its convex counterpart, it still lacks a general and principled framework.NormDetect: Anchored Calibration for Low-False-Positive Detection of Machine-Generated Web Text
Web platforms screen posts for text written by large language models, and every false flag hits a human writer.RetryRoute: Retry-Aware Model Routing for Agentic Image Editing Services
Web agents increasingly edit images by calling open editing models in a critique-and-retry loop, and these models differ in cost by more than an order of magnitude.When Links Mislead: Label-Free Structural Polarity for Zero-Shot Classification of Web Pages
Web platforms such as digital libraries, online stores and university sites must classify new pages before anyone labels them.Spectral Haar Binarizer: Compact Boolean Contrast Representations for Hyperspectral Classification
Tsetlin Machines offer an interpretable, hardware-friendly alternative to conventional machine learning models, but applying them to continuous hyperspectral measurements requires converting…KV-Cache Patching: Tracing How and Where Fine-Tuned Behaviors Arise
Fine-tuning a language model changes how it responds, but where is this fine-tuned behavior instantiated?PCD: Predictive Coding Dynamics for Irregularly Sampled Time Series
Dynamic systems evolve continuously over time, whereas observations in real scenarios are often sparse, irregular, and asynchronous.Infinite ResNets are Gaussian Mean ODEs
Understanding the training dynamics of deep neural networks in large-scale limits is a question both of foundational and of practical importance.Enhancing Sentiment Classification on Small Datasets through Data Augmentation and Transfer Learning
Small-scale sentiment classification often suffers from data scarcity, which limits the generalization ability of the models.DAPS: Dependency-Aware Premise Selection for LLM Theorem Proving
LLM-based theorem proving in Lean 4 has advanced rapidly, but provers are difficult to leverage newly contributed lemmas, because retrieval skews toward popular foundational lemmas, leaving the…FOCUS: First-Order Control Using Optimal Transport Supervision for Imitation from Observations
Imitation learning from observations must recover behavior from state-only demonstrations without known temporal correspondence.DeepWeave: Weaving Neural and Symbolic Evidence for ML-Based Network Intrusion Detection System
Machine learning-based network intrusion detection systems can recognize complex traffic patterns, but often require substantial labelled attack data, degrade under deployment shift, and provide…ORION: An Outcome-Driven LLM Advisor for RL-Based Multi-Domain 6G Slice Placement
Deploying 6G network slices across administrative domains requires an online decision for each arriving request: which domain hosts each virtual network function, how traffic crosses domain…BYOD: Build Your Own DLM - Efficient Autoregressive-to-Diffusion Language Model Conversion
Unlike autoregressive (AR) models, diffusion language models (DLMs) permit parallel token updates, flexible generation, and bidirectional attention, but leading systems rely on costly pretraining or…Reliability-Weighted Direct Preference Optimization: Penalized Weights for Robust Preference Learning
Direct Preference Optimization (DPO) treats every reported comparison as correct, yet preference data contain reversed, tied or otherwise noisy comparisons.PDE-Residual-Informed Kriging
Physics-informed Gaussian process (GP) methods provide a probabilistic framework for PDE solving.Underdamped Stochastic Optimal Transport with Landauer boundary conditions: machine learning and analytical solution
Finding optimal transport protocols for underdamped stochastic processes is a difficult yet important problem in control theory.What Carries Architectural Signal? A Code-to-Architecture Mapping Study
Code-to-architecture mapping assigns source files to modules of a reference architecture.Co$^2$ODE: Conditionally Coarse-Grained Model Discovery for Rewiring in Biochemical Networks
Biochemical networks propagate the signals that regulate cellular behaviour, and which routes carry them depends on cellular context: a shift in context rewires the network's effective dynamics.