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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 130
Bifurcations from Weight Space: JEPAWG on Parametric Dynamical Systems
Trained network weights are increasingly treated as data, but there is rarely ground truth about what a population of models should encode, which hampers systematic validation of weight-space methods.Training Mixed-Monotone Neural Networks for Verification Without Set Propagation
Most formal verification approaches for neural networks conservatively enclose the output set of the neural network.Joules-to-GDP: How Much Economic Value Do AI Systems Produce per Unit of Energy?
An AI system can produce more economically valuable work while delivering less value for the energy it consumes.MOER: Mix-Policy Optimization under Expert Prefix Guidance with Selective Rollback
Mix-policy optimization incorporates off-policy expert data into the reinforcement learning (RL) loop to transcend the capability boundary of on-policy RL.One Teacher, Many Views: Reducing Reference Sensitivity in On-Policy Self-Distillation
On-policy self-distillation (OPSD) improves reasoning by training a model on its own trajectories with dense token-level supervision from a privileged copy conditioned on a reference solution.Inherited, Not Invented: Tracing the Provenance of Wait in Reasoning Models
Reinforcement learning (RL) is credited with teaching reasoning models to write {wait} before reconsidering a step, yet large RL accuracy gains do not require the word.Tracing Subliminal Learning Through Pretraining
Large language models trained on data generated by other models can sometimes inherit traits unrelated to that data, a phenomenon called subliminal learning.Building Presentation Intelligence for High-Quality Slide Generation
PowerPoint presentations (PPTs) are a dominant medium for communication across diverse domains, yet high-quality PPT generation still depends heavily on labor-intensive human effort.Continuous-depth Deep Gaussian Processes
Deep Gaussian processes (DGPs) are appealing Bayesian regression models, but their standard discrete-depth parameterization often becomes harder to optimize as depth grows.Equilibria and Learning in Competitive Market Making
We propose a market-making game played among $N \ge 2$ strategic market makers and a single non-strategic representative trader.Parameter Learning in Active Inference via Variational Message Passing
Active inference casts perception, learning and planning as variational inference, where the Expected Free Energy (EFE) defines the planning objective.Same molecule, Different Knowledge: Cross-Identifier Binding Failures in LLMs For chemistry
Chemical AI workflows routinely represent the same molecule through multiple co-referring identifiers, including trivial names, IUPAC names, and SMILES, implicitly assuming that these…MIG-COW: Transferable Adversarial Attacks on Deepfake Detectors via Gradient Decomposition
Despite recent advances, deepfake detectors remain vulnerable to adversarial examples, particularly in diverse, real-world settings.Bridging Number-Theoretic Primality Testing And Algorithmic Optimization: An Adaptive Cryptographic Framework
The security of modern public-key cryptographic systems, such as RSA, Diffie-Hellman, and ECC, depends entirely on the computational intractability of the integer factorization and discrete…Vision Transformers for Estimating Irradiance Using Data Scarce Sky Images
Accurate estimation of diffuse horizontal irradiance (DHI) is critical for optimising photovoltaic system performance and energy forecasting yet remains challenging in regions lacking comprehensive…PAIR: Preference-Aligned Intent Retrieval for Premium People Recommendation at LinkedIn
General-purpose embeddings trained on profile similarity often misrank candidates in professional networks, where relevance is inherently complementary rather than symmetric.GateFormer: Forecast-Space Decomposition for Intra-Hour Solar Irradiance Prediction from Sky Images
Sub-hourly dispatch, balancing, and storage control increasingly depend on forecasts that anticipate rapid variability in solar generation.Random Projections for Spectral Algorithms: Optimal Rates in Sobolev Norms
Random projections (RP) offer an effective method for reducing computational and storage overhead while preserving the geometric structure of data.Random Initialization as a Baseline for Time Series Foundation Models
Time series foundation models (TSFMs) use self-supervised pretraining to learn reusable representations for downstream tasks such as multivariate time series classification (MTSC).OpenAttention: One Percent Is Enough with Tail Proxies
Sparse attention reads only a small subset of the key-value cache, and how to treat the unread tail remains a key challenge.