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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 241
Silence and Noise: Resolving Suppression and Overvaluation in Multimodal Entity Alignment
Multimodal entity alignment (MMEA) is fundamental to integrating heterogeneous knowledge graphs (KGs), yet existing paradigms remain critically vulnerable to incomplete modalities and cross-graph…Elephant in the Fridge: Constant-Memory Frame Packing for Long Video Understanding
Hour-long videos, streaming feeds, and movie-length content are pushing vision-language models (VLMs) past their breaking point -- the standard recipe of encoding each frame into visual tokens…PID-Gated Continual Prompt Adaptation Under Noisy Feedback
Deploying LLM agents in non-stationary environments requires continually revising their instructions as the task drifts, yet rewriting after every reported mistake can turn noisy feedback into…XForecast: The KDD 2026 Finance Day Challenge on News-Grounded Financial Forecasting
Financial forecasting has traditionally relied on numerical time-series (TS) signals, while textual information such as news remains largely underutilized.Distributional Regression for Arbitrary Response Variables Using ReLU Neural Networks
This paper develops a unified framework for distributional regression with arbitrary response variables.BARTER: Compact Dynamic Sparse Training with Standard Dense Kernels
Dynamic sparse training (DST) aims to reduce training costs while maintaining accuracy.Equilibrium Learning in Unbounded Stochastic Games with Gaussian Transitions
Learning equilibria from data is difficult when several decision makers interact indefinitely and the system state has no prescribed bounds.WildTrace: Evaluating Training Data Detection in Realistic Large-Scale LLM Post-Training
This drives growing interest in training data detection for post-trained models.Fisher information and the geometry of memorization in neural networks
A fruitful approach towards understanding generalization in machine learning involves characterizing how models separate generalizable structure from idiosyncratic or even corrupted features of…Think Before You Speak: Enabling Training-Free Latent Communication Between Language Models
Large language models (LLMs) have demonstrated remarkable capabilities in solving complex tasks in coding, mathematical logic, and iterative reasoning, especially by coordinating effectively within…Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction
We propose the task of Interactive 4D Volumetric Liquid Forecasting: predicting the spatiotemporal evolution of a future dense 3D liquid velocity field conditioned on how a solid object moves…Exploration strategies for Gaussian process bandit optimization with inference regret guarantees
Existing cumulative regret guarantees for Bayesian optimization (BO) do not necessarily characterize the quality of the algorithm's final output.MSC: Multi-Stage Caster-aware Control Framework with Uncertainty-Aware NMPC and RL-based Execution Layer
Freely swiveling casters are widely adopted in industrial AMRs/SDVs for their mechanical simplicity and load capacity.MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification
Spatial transcriptomics jointly captures molecular state, tissue morphology, and spatial organization, yet spatial-domain methods are typically compared only at late training checkpoints, obscuring…OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection
Time series (TS) anomaly detection (AD) lacks a universal detector, with the best choice determined by the series rather than by the algorithm.When Participant Disjoint Validation Is Not Enough to Establish Generalization in Parkinson's Gait Classification
Participant-disjoint cross-validation prevents direct participant overlap, but it may still produce optimistic estimates when acquisition site and diagnosis are structurally dependent.NetRL: Powering Network Decisions via NetGRPO-Driven Reinforcement Learning
Network decision-making tasks, including adaptive bitrate streaming, load balancing, and congestion control, require policies to process heterogeneous states and actions while balancing near- and…AutoNovo: Agentic Program Search Discovers Transferable Nucleic Acid Design Algorithms
In-silico optimization of nucleic acids relies on predictive oracles that imperfectly capture biological function, so sequences optimized against one model may exploit model-specific artifacts and…From Simulated Fluids to Living Tissue: Zero-Shot Foundation Model Transfer to Perturbed Embryos
Morphogenesis is driven by the interplay of genetic regulation and tissue mechanics.Reflected Proximal Dynamics for Accelerated High-Accuracy Log-Concave Sampling
We study sampling from $π(x)\propto\mathrm{e}^{-V(x)}$ on $\mathbb{R}^d$, where $V$ is $α$-strongly convex and $β$-smooth with condition number $κ=β/α$.