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The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 95
Selective and Hybrid-State Communication for Latent Multi-Agent Systems
Efficient communication remains a major bottleneck in large language model multiagent systems.Spokes, not a Ring: Revisiting the Weekday Manifold in Language Models
Recent work has hypothesised that large language models may represent some concepts as multi-dimensional manifolds instead of single directions.Responsible Quantum Machine Learning for Medical Imaging: A Fairness Audit Across Sensitive Subgroups
Quantum machine learning (QML) is increasingly explored for medical classification, yet its behaviour under subgroup-aware fairness criteria remains understudied.Learning Without Clocks: Partition-Invariant Neural Network
Event cameras, spiking cochleas and similar sensors emit asynchronous events, yet most learning systems first bin them into frames or packets, so the output depends on how the stream was sliced.Learning Lotteries with Minimal Stakeholder Violations from Membership Queries
An AI developer often has several candidate versions of a system and can deploy a mixture of them, sending each request to a randomly chosen version, as in A/B testing.Depth through Recurrence: Towards Ultra-Efficient On-Device ASR
Modern automatic speech recognition (ASR) systems have achieved remarkable accuracy by scaling model depth and capacity, but at the cost of increased memory, energy, and computational demands.Investigating the Early Signatures of Feature Learning in Deep Linear Networks
Deep neural networks learn data-dependent features, a capacity widely credited for their empirical success.Improving Data-Constrained Scaling with Hyperparameter Transfer and Ensembling
As growing compute budgets outpace the growth of high-quality training data, large language models must be trained with both compute and data constraints in mind.An $Ω(T^{3/4})$ lower bound for projection-free online learning with linear minimization oracles
This work considers projection-free online learning algorithms with linear minimization oracles for handling the decision set.Finite-Sample Optimal Conformal Prediction Sets under Covariate Shift
We study conformal prediction under covariate shift with a known covariate density ratio, seeking prediction sets with minimum expected length under the target distribution.Multi-Agent Reinforcement Learning for Global Climate Policy Cooperation
International climate policy requires coordination among countries whose mitigation decisions impose local costs but generate global benefits.Interpretable neural equation learning for high-dimensional materials data
Equation learning seeks to discover compact, interpretable equations from data using various machine-learning techniques.When More Planning Hurts: Learned Planners for World Models
On PushT, our fixed action updater (1,505 parameters) solves 8 of 16 scenes reserved from our configuration selection.SAC-GCL: Structure-Aware Compression for Graph Contrastive Learning
Graph Contrastive Learning (GCL) has achieved remarkable success in self-supervised representation learning by maximizing agreement between augmented views of a graph.Think Twice: The Metis-1.6 Technical Report
Metis is a bet that better data, better training methods and new architectures can buy what a bigger parameter count cannot, at a fraction of the cost.TERRA: Task-Oriented Edge AI for Regional Fire Detection with Identity-Free Reporting
Distributed fire-monitoring systems may involve many sensing devices reporting over a shared uplink, even though the operational objective is often to identify regions exhibiting evidence of fire…Generative modeling for fusion: a large-scale benchmark of stellarator fast-ion simulations
Stellarators offer a promising magnetic confinement approach for achieving controlled nuclear fusion.Muon Makes the First Mixture-of-Experts Layer Harder to Balance
Many open mixture-of-experts (MoE) language models train with Muon instead of AdamW.What Actually Matters? A Paired Evaluation of Inference Settings for $π_{0.5}$ on BEHAVIOR-1K
Vision-Language-Action (VLA) models offer a promising paradigm for general-purpose household robotics.FAD-KV: Adaptive Dictionary KV Caching for Efficient Reasoning Decoding
Key-value (KV) caching is a core mechanism in large language models, where intermediate key and value representations are stored to accelerate autoregressive decoding.