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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 131
Ground-Truth-First Benchmarking of Reinforcement Learning via Converse Optimality: From Smooth, Fully Observed Systems to Non-Smooth or Partially Observed Tasks
Benchmarking in reinforcement learning is usually relative: algorithms are ranked by returns on environments with unknown optima.Doubly Robust Distributionally Robust Offline Contextual Pricing
Offline contextual pricing often relies on logged observational data, but faces challenges from distributional shifts between training and deployment environments.ReDence: A Hybrid Agentic and Decision-Model Framework for Rapid, Evidence-Grounded Detection of Modern Social Engineering Scams
Detecting online social engineering (SE) scams often requires examining website workflows, verifying claims against external sources, and connecting evidence across multiple pages.Energy and Latency First: How Deployment Constraints Reverse Time-Series Model Selection
AI models, and time-series models in particular are typically ranked by accuracy and deployed under constraints absent from that ranking.Joint or Per-Option? Compact Encoders for Closed-Set Decisions from 17 Million to 1 Billion Parameters
Many language-model pipelines end in a closed-set decision, such as selecting a tool.Diagnostic Loss: Asymmetric Supervision for Radiology Report Generation
Automated radiology report generation requires models to do more than produce fluent clinical narratives: they must explicitly state supported diagnoses while avoiding unsupported ones.EEG-Captioner: Grounding EEG-to-Language Generation in Fundamental Signal Properties
Electroencephalography (EEG)-to-language models offer a promising approach for translating complex neural signals into human-readable descriptions.XALIGN: A Human-centric Visual Analytics Approach to Understanding AI Explanations
Explainable AI (XAI) methods are usually evaluated for technical properties such as faithfulness and robustness.Mostly Reading: What Open-Book Scores Measure in Document-Level LoRA Adapters
Single-document LoRA adapters have been proposed as units of knowledge to be merged and retrieved; yet open-book scoring, with the document in the prompt, can credit a document's own adapter (its…NN-Based Transistor Modeling for Ultra-Fast SPICE Simulation and Standard Cell Library Characterization
Neural network (NN)-based transistor models have proven to be a promising solution to accelerate device modeling.Federated Differentially Private Optimization via Low-Rank Preconditioning
We consider differentially private (DP) optimization in the federated setting, where a DP estimate of the feature covariance matrix serves as a Hessian approximation for generalized linear models.When Forgotten Facts Return: Relearning-Aware Unlearning of Knowledge Graph Embeddings via Tangent Kernels
Approximate unlearning answers a deletion request on a knowledge-graph embedding by editing entity embeddings instead of retraining from scratch.nnInteractive Agent: General-Purpose AI Agents as 3D Image Annotators
Interactive 3D segmentation models such as nnInteractive turn a few spatial prompts (points, scribbles, boxes) into accurate volumetric masks, but a human still does the annotation work: finding…Transformers with CoT Discover Heavy-Ball Acceleration Through Training
Modern transformers often solve complex tasks by generating and refining intermediate reasoning steps.DRAFT: Decoding and Reconstructing 3D Room Layouts from fMRI and Camera Trajectories
While functional magnetic resonance imaging (fMRI) decoders reconstruct images and 3D objects, recovering the metric geometry of rooms from a moving viewpoint remains an open challenge.What Comes Next and Why: Interpretable Next-Event Prediction with Neuro-Symbolic Rules
In real-world applications, being able to explain why a prediction is made is often as important - if not more so - than achieving high accuracy.Delta N-gram: Forward-Only Continual Writing into a Language Model's Native N-gram Memory
Our previous analysis found that a model's native hashed N-gram table is where continual updates disturb the base least, but its writes needed backpropagation and changed the pretrained table.Post-Audit Mirage: Identifiability Limits of Offline Agent Verification
Offline tests can fail to reveal whether an agent update will remain safe after deployment.Is High Local Accuracy Enough? Geometric Consensus with Support-Adaptive Statistical Personalization for Federated Learning
Real-world deployment of personalized federated learning (PFL) requires adapting to heterogeneous clients while preserving recognition performance beyond local distributions.Mixing Matters: Evidence Position Bias Across Sequence Mixers in Long-Context Question Answering
Long-context models often use answer-bearing evidence less effectively when it appears away from a prompt boundary.