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Machine Learning
The newest papers in Machine Learning that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 21
Integrating Machine Learning and Uncertainty Quantification for Real-Time Decision-Making in Digital Twins: A System-Centric Review and Perspective
Digital Twins are evolving from passive virtual representations into closed-loop systems that continuously synchronize with physical assets and drive real-time decisions.Deep Learning Compilers: Architecture, Optimization, and Empirical Characterization
As deep learning (DL) models become increasingly complex and hardware architectures grow more heterogeneous, DL compilers play a critical role in optimizing computational efficiency and enabling…How Risky Is Your Data-Driven Policy?
Data-driven algorithms are ubiquitous across industries, and there is an extensive literature on analyzing their \emph{average} performance.Reciprocity Can Erase Direction:tructural Boundaries and Controlled Utility of Magnetic Graph Networks
Magnetic graph networks encode direction through phase, but a learned graph can erase the intended directional mechanism before prediction.Nearly Linear-Time Follow-the-Perturbed-Leader for Weakly Observable Graph Bandits
We study online learning with directed weakly observable feedback graphs and propose a Follow-the-Perturbed-Leader (FTPL) policy that achieves best-of-both-worlds guarantee with nearly linear…What Is the Influence of Task Order in Continual Learning? An Empirical Study and Order-Aware Evaluation for Motion Forecasting
Aggregate scores for continual learning algorithms rest on a distribution over task orders that is rarely stated.Coarse Semantic Blocks for Efficient Training-Free Sparse Attention
We introduce a training-free sparse attention method that builds a prompt-specific key index to accelerate both prefill and decoding.Anchor: Augmented Cross-Layer Recurrent State for Long-Context Retrieval in Test-Time Training
Parameter-efficient linearization turns a pretrained autoregressive Transformer into an efficient model by hybridizing sliding-window attention (SWA) with a constant-size recurrent memory.CORA: Teacher-Guided Causal O-RAN Adaptation for Jamming Detection
In mission-critical networks, a missed jamming event is costlier than a false alarm, so detectors should favor recall, operate causally, and work without labeled attack data.Proper Agnostic Learning of Affine Halfspaces under Log-Concave Marginals
We give a proper constant-factor agnostic learner for affine halfspaces under full-dimensional log-concave marginals.Sparse Internal Control of Language Models
Language models are increasingly used in text generation, decision support, and automated interaction, creating a need for localized, reversible, and selective control of their behavior.Benchmarking Unsupervised Jamming Detection in 5G Open RAN
Radio Frequency (RF) jamming threatens fifth-generation (5G) networks.Aligning Training and Rollout Policies for End-to-End FP4 RL on Mixture-of-Experts LLMs
In this work, we study end-to-end FP4 reinforcement learning (RL) post-training for Mixture-of-Experts (MoE) large language models.Spin Spherical Fourier Neural Operators: Efficient Equivariant Learning for Vector and Spin Fields on the Sphere
Many fields of physical interest on the sphere, such as wind, the polarisation of the cosmic microwave background and the shear of weak gravitational lensing, are spin fields, whose values change…Aero-VPA: Few-Step Flow-Matching Control over Non-Reconstructive JEPA Latents for Agile Flight on Edge Hardware
Vision-Language-Action (VLA) models generalize across tasks, but their autoregressive decoders need sequential forward passes in proportion to the action horizon, each bounded below on embedded…Voluntary Servitude: Weaponized Safety Warning Mechanisms for Controlling AI Agents
LLM agents increasingly act on information retrieved from public sources they treat as credible, which turns a manipulated recommendation into a transaction, a dependency, or a decision with…SWAP-INR: Implicit Neural Representation with Switchable Attention Paths for Time-varying Multivariate Scientific Data Compression
Visualizing multivariate scientific simulation data is essential for understanding complex physical phenomena, yet the massive scale of such data imposes a severe storage bottleneck in…Sketchpad: certified exact arithmetic with a small vision-language model
Language models are accurate on most long-arithmetic problems, but their accuracy is estimated on samples and carries no guarantee for an unseen input; tool use is exact but moves the computation…Near-Zero Token Embeddings Silently Stall Training in Gated Sequence Models
Among 291 surveyed open-weight language model repositories (280 distinct embedding tables), 52.6% of untied gated and hybrid checkpoints (20/38) contain reachable input embeddings with norms below…SaMA: Morpho Adaptation via Asymmetric Expansion of Kronecker Product
Low-rank adaptation (LoRA) has attracted significant attention in the parameter-efficient fine-tuning (PEFT) landscape.