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 92
EmbeddingGemma 2: Unified and Efficient Multimodal Representations
We present EmbeddingGemma 2, an open multimodal embedding model which natively represents text, images, audio, video, and combinations thereof in a unified space.TRIDENT-Seq: Profile-Matched Prefix-Future Contrast for Label-Efficient Payment Risk Modeling
Ant International accumulates large volumes of transaction history across users and payment businesses.The Geometry of Manifold Learning: Singular foliations via Deep Learning models
Deep learning algorithms have become widely used and successful across a wide range of tasks.Euclidean Crowding-Driven Adaptive Euclidean-Hyperbolic Graph Learning
Hyperbolic graph representation learning has emerged as a powerful paradigm for modeling hierarchical data.Agent-Conditioned Routing: Diverse Reasoning Paths Within One Frozen Model
Generating several solutions can expose alternative methods, but additional trajectories often paraphrase one dominant approach.PDRVLA: A Vision-Language-Action Model with Physical Dynamics Representations
Vision--Language--Action (VLA) models generate robot actions from visual observations and language instructions, but their understanding of physical dynamics remains limited.Channel Selection for Mixture-of-Experts Decoding Needs No Calibration Data
Mixture-of-Experts (MoE) models are bottlenecked by loading routed-expert weights from memory during interactive decoding at small batch sizes.Para-Gaussian Approximations of Random Deep Neural Networks
It is known that randomly initialized neural networks have Gaussian behaviour in the $n\to \infty$ limit.How Many and How Much? Contrastive Learning with Negative Samples for Knowledge Graph Embedding
Knowledge graph embedding (KGE), a fundamental technique for knowledge-centric Web and KG-augmented AI applications, is commonly trained through contrastive learning with negative samples to learn…LinMix: Data Mixing as Linear Domain Attribution
Language model performance depends strongly on the mixture of domains in the pretraining data corpus.Learning Quality-Controllable Robot Policies with Iterative Hindsight Relabeling
Robot policy evaluation often relies on a binary success signal for task completion.An end-to-end framework for uncertainty-aware validation of generative models
Density estimation with generative models is a common task in the physical sciences, for example in background estimation in particle physics.Displacement Moments and Path-Terminal Separation in Brownian Interpolant Sampling
Brownian stochastic interpolants connect paired endpoint samples, yet their sampling error can depend on displacement moments even when neither endpoint has a first moment.Bayes-Adaptive MDPs for Rollout Allocation to Enhance GRPO Training
Group Relative Policy Optimization (GRPO) has demonstrated strong performance in LLM post-training.When Visual Evidence Is Insufficient: How Instructions, Post-Training, and Internal Activations Shape Refusal in Multimodal Language Models
For reliable deployment, multimodal large language models (MLLMs) should recognize when an image provides insufficient evidence to answer a question and withhold unsupported answers.Multi-Agent Reinforcement Learning of Co-Adaptive Hip Exoskeleton Control (MARL-Exo) for Metabolic Energy Reduction
This study aims to determine whether a hip-exoskeleton policy trained in simulation, by co-adapting with a musculoskeletal human model under a metabolic-energy reward, reduces the metabolic cost of…CRISP: Co-designing Pruning and Scheduling for Efficient MoE Inference on Edge Servers
Mixture-of-Experts (MoE) inference on memory-constrained edge platforms is often dominated by expert-weight movement rather than computation.Graph Representation Learning via Adaptive Expectile Aggregation
Graph neural networks (GNNs) rely critically on neighborhood aggregation, yet conventional mean aggregation may fail to capture class-relevant distributional information, particularly in…Interpretable Meta-Policies for Neural Decision Making
Deep Reinforcement Learning remains difficult to interpret and align due to the black-box nature of neural networks.An Input Skip Can Reparameterize a VAE Without Improving the Generator
An input skip can reduce reconstruction error while leaving a model's distribution of new samples unchanged.