AI & Machine Learning · Computer Science & AI
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.
Get the few papers worth your time in Machine Learning every morning.
Follow Machine Learning and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 242
Hardware-Friendly Token-Group Activation Quantization for Low-Bit Mamba Super-Resolution
Mamba-based super-resolution models are efficient alternatives to Transformer-based restoration backbones, but their low-bit post-training quantization remains challenging because selective scan…Spectral Considerations on Unsupervised Time-domain EEG Representation Learning with Deep Networks
Electroencephalography (EEG) is a non-invasive, scalable source of quantitative biomarkers for the early diagnosis of brain disorders, and deep representation learning has become a powerful paradigm…Error Correction in Trained TTT Layers
Test-time-training (TTT) layers update fast-weight memory while processing an input sequence.Deep learning methods for gamma event selection in TAIGA-IACT image analysis in stereo-mode
In astrophysical complex TAIGA there are four Imaging Atmospheric Cherenkov telescopes (IACTs), located at a distance of 300-500 m from each other, due to which extensive air showers (EAS) generated…Deep Speech Representations Track Word Timing along a Low-Dimensional Circular Trajectory
Speech timing is fundamental for speech models to recognize words accurately, but how word-timing information is represented in speech models remains underexplored.A Survey of Flow Matching in Reinforcement Learning
Flow Matching (FM) has recently emerged as a principled and efficient generative modeling framework for reinforcement learning (RL), enabling expressive, multimodal policy parameterizations via…agent-risk: Risk Assessment for Changes to AI Agents
Before changing a deployed AI agent (a stricter safety gate, a new model), organizations must approve the change, starting from existing logs.Moment-Based Anomaly Detection in High-Dimensional Mixed Data
We propose an anomaly detection method for high-dimensional mixed-type data that maps heterogeneous features to a common scale via standardized moment-based transformations, yielding interpretable…Fast Quantized Johnson$-$Lindenstrauss Transforms
Quantization has emerged as one of the primary techniques for reducing the memory footprint of large language models during inference.SLDBench v2: Active Scaling Law Discovery
Scaling laws predict large-model performance from small experiments, but finding a reliable law itself requires costly experiments.BehaviorFormer: Discovering Behavioral Patterns via Clusterable Next-Token Prediction
Discovering behavioral patterns in open-ended games is challenging due to the lack of clear objectives or ground-truth annotations.Probabilistic Simulation of Gas-Liquid Interfaces with Coupled Flow Matching on Spherical Harmonics
Bubbly flows involve strongly coupled bubble motion, deformation, and hydrodynamic interactions, making high-fidelity simulation computationally expensive.Efficient Multi-Agent Reasoning via Confidence-Guided Adaptive Debate
Multi-agent debate has shown promise for improving the reasoning of large language models, yet recent theory suggests its benefits are highly regime-dependent.ChronoScribe: Semantic-Numerical Specialization for Instruction-Driven Time Series Forecasting
Recent advances in instruction-driven time series forecasting highlight a fundamental tension: systems must simultaneously master natural-language instruction following and precise numerical…Rescuing Points Trapped Inside Incorrect Decision Regions: A target for Evaluating and Improving Forward-Forward Learning
Forward-Forward learning replaces end-to-end backpropagation with local, layer-wise training objectives.DWI Connectome-based Sex Classification in Young using Supervised Variational Autoencoder
During adolescence, significant changes in brain development are influenced by hormonal changes that impact both the body and the brain.RIFT: Token Influence Follows the Path of Least Resistance
Which context tokens drive a language model's answer?A Production-Grade, High-Efficiency Agentic RL Framework for Frontier LLMs with Validated Recipes
Reinforcement learning (RL) has emerged as a critical phase in the post-training pipeline of large language models (LLMs).naspaclust: A hybridisation of spatial fuzzy clustering with nature-inspired optimisation
We present an overview of R package called \texttt{naspaclust}, a tool for spatial fuzzy clustering improvement by hybridising the Fuzzy Geographically Weighted Clustering (FGWC) with…LoopQuant: Accurate and Efficient Post-Training Quantization for Looped Transformers
Looped Language Models (LoopLMs) improve parameter efficiency by recursively reusing shared Transformer modules, yet their repeated computation over evolving recurrent states poses new challenges…