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NEMORA: Neural Equivariant Multipole Operators for Long-Range Atomistic Learning

arXiv:2610.10776v1 Announce Type: new Abstract: Equivariant graph neural networks have emerged as foundational architectures for machine-learned interatomic potentials, approaching quantum-chemical ac

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Networks with Finite VC Dimension: Pro and Contra

DGX agent

arXiv:2502.02679v3 Announce Type: replace-cross Abstract: Approximation and learning of classifiers of large data sets by neural networks in terms of high-dimensional geometry and statistical learning

researcharxiv-cs-lg

9 Oct 2026

Neural Architecture Discovery via Autonomous Evolution

arXiv:2507.18074v2 Announce Type: replace Abstract: Recent progress in LLM agents has advanced the prospect of autonomous research. Yet whether AI can complete difficult long-horizon tasks, especially

Neural Decoding as Cognitive Inference

arXiv:2610.11923v1 Announce Type: cross Abstract: The brain maintains stable cognition despite continuously changing neural activity. How to extract stable cognitive states from variable neural observ

tutorialsarxiv-cs-ai

Neural Network Verification for Deep Joint Source-Channel Coding

arXiv:2610.11994v1 Announce Type: cross Abstract: Deep joint source-channel coding (DeepJSCC) transmits data end-to-end over wireless channels using a neural encoder-decoder, but reconstruction qualit

Neural Networks for Temporal Pattern Recognition and Dynamic Arm Gesture Speed Estimation for Robot Control

arXiv:2610.11631v1 Announce Type: cross Abstract: Deploying intelligent robotic systems that interact with humans through gestures requires neural networks capable of recognizing diverse temporal patt

Neuro-Memory Fuzzy Inference System for Mimicking Human-like Car Following Behavior

arXiv:2610.11252v1 Announce Type: cross Abstract: This study presents the Neuro-Memory Fuzzy Inference System (NeMeFIS), a hierarchical machine learning architecture that asymmetrically models acceler

New Lower Bound and Upper Bounds on the Regret for Online Sparse Linear Regression

arXiv:2610.11551v1 Announce Type: new Abstract: We study online sparse linear regression (OSLR) where any algorithm is restricted to accessing only b out of d attributes per instance for prediction an

No Distillation Needed: Single-Pass Real-Time Talking Heads via Acausal Noise Shaping

arXiv:2610.11070v1 Announce Type: new Abstract: Audio-driven facial animation underpins real-time avatars, telepresence, and embodied virtual agents. And it must run online: each frame emitted from au

researcharxiv-cs-cv

Noise-Induced Navigation in Non-convex Domains and Compact Manifolds

arXiv:2610.10949v1 Announce Type: cross Abstract: In this note, we study the problem of designing a feedback law that globally steers a system to a prescribed target configuration. Even if the system

researcharxiv-cs-ro

NOMOS: Compiling Written Policies into Statically Verified Tool-Call Gates for LLM Agents

arXiv:2610.11030v1 Announce Type: cross Abstract: Tool-using LLM agents violate the policies they are deployed to enforce, often silently. Prior defenses hand-write rules, query an LLM verifier per ac

Local Ai

Not Every Change Is Necessary: Recoverable Drift in Large Language Model Unlearning

arXiv:2610.11915v1 Announce Type: new Abstract: Machine unlearning in large language models aims to remove unwanted knowledge while preserving the model's remaining capabilities. Although existing met

local-aiarxiv-cs-cl

NP-Hardness of Minimizing Neurons in Two-Hidden-Layer ReLU Neural Networks

arXiv:2610.11313v1 Announce Type: new Abstract: A fundamental question in neural network architecture optimization is whether the minimum hidden-neuron count required to approximate a target function

Nullify: Null-Space Activation Steering for Training-Free LLM Unlearning

arXiv:2610.10655v1 Announce Type: cross Abstract: Large Language Models (LLMs) inevitably internalize substantial amounts of sensitive or private information during pre-training, while LLM unlearning

OA-MAP: Evidence-Grounded Multi-Agent Multimodal Framework for Interpretable Knee Osteoarthritis Progression

arXiv:2610.12134v1 Announce Type: new Abstract: Knee osteoarthritis (KOA) progression prediction can support patient monitoring, requiring the integration of multimodal data and multidomain expertise.

Obscuring Data Contamination Through Translation: Evidence from Arabic Corpora

arXiv:2601.14994v2 Announce Type: replace-cross Abstract: Data contamination can invalidate benchmark evaluation when a model benefits from memorized evaluation content rather than genuine generalizat

Omni-Diffusion-Distill: Few-Step Distillation of Unified Multimodal Diffusion Large Language Models

arXiv:2610.10990v1 Announce Type: new Abstract: Unified multimodal diffusion large language models (dLLMs) offer a single architecture for both image generation and multimodal understanding, but their

tutorialsarxiv-cs-cv

OmniCapBench: A Deep-Structured Evaluation Framework for Fine-Grained Audio-Visual Captioning

arXiv:2610.12458v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are rapidly evolving toward continuous audio--visual reasoning, creating an urgent need for evaluations that ex

OmniDex: Scaling Dexterous Hand Grasping to Diverse Cluttered Scenes

arXiv:2610.11194v1 Announce Type: cross Abstract: Dexterous grasping is the foundational primitive in embodied AI, demanding massive data to train robust models. As real-world data collection is expen

OmniHOI: Dexterous Hand-Object Interaction from Monocular Human Video

arXiv:2610.10855v1 Announce Type: new Abstract: Monocular videos of human manipulation provide abundant dexterous demonstrations, yet reconstructing hand-object interaction from a single view and tran

safetyarxiv-cs-ro

On linearity or non-linearity in machine learning for quantum chaotic dynamics

model-releasesarxiv-cs-lg

arXiv:2610.10697v1 Announce Type: cross Abstract: Accurately simulating chaotic quantum many-body dynamics remains a major computational challenge for classical methods, due to the rapid buildup and s

On the Clock: Towards Punctual and Productive Time-Budgeted AI Agents

arXiv:2610.10833v1 Announce Type: new Abstract: We study whether small LLM agents can operate effectively under explicit wall-clock time budgets by both respecting the allocated runtime and using avai

On the estimation and validity of AI time horizons---a statistical look at the METR plot

arXiv:2610.12466v1 Announce Type: new Abstract: METR's 50% time horizon measures the human completion time of software tasks that an AI solves with 50% probability, allowing AI capabilities to be expr

Onboard Marine Anomaly Detection on Phisat-2: From Simulation-Based Development to In-Orbit Demonstration

arXiv:2610.11735v1 Announce Type: new Abstract: Onboard Artificial Intelligence can improve responsiveness and bandwidth efficiency of Earth Observation systems by processing data directly on the sate

One Block, Multiple Depths: Recurrent Vision Transformers with Depth-Programmed Experts

arXiv:2610.12448v1 Announce Type: new Abstract: In this work, we show that a single Transformer block, applied recurrently, can match the accuracy of a full-depth vision encoder at comparable inferenc

One Permutation Is All You Need: Fast, Deterministic Feature Importance and Model Stress-Testing

arXiv:2512.13892v3 Announce Type: replace-cross Abstract: Reliable estimation of feature contributions in machine learning models is essential for transparency, algorithmic fairness, and regulatory co

One Skill Too Many: How Co-Installed Skills Conflict in Coding Agents

arXiv:2610.11647v1 Announce Type: cross Abstract: Coding agents are extended with agent skills, directories whose SKILL.md tells the model when and how to perform a task. Because skills come from inde

One Word Opens the Gate: The Option-Channel Attack on Typed Decision Models as Agent Guardrails

arXiv:2610.12292v1 Announce Type: new Abstract: A typed decision model reads a piece of text and returns a probability over caller-defined options, each with a short written definition, generating no

$OneMillion-Bench: How Far are Language Agents from Human Experts?

arXiv:2603.07980v2 Announce Type: replace-cross Abstract: As language models (LMs) evolve from chat assistants to long-horizon agents capable of multi-step reasoning and tool use, existing benchmarks

OneSearch-VL: Unified Multimodal Deep Research Agent for Image and Video

arXiv:2610.12419v1 Announce Type: new Abstract: Single-image, multi-image, and video deep research require different visual operations but share a workflow of visual grounding, external retrieval, and

agentsarxiv-cs-cv

OnTrack: Real-Time Monitoring and Intervention in LLM Agent Trajectories via Streaming Structure-Aware Optimal Transport

arXiv:2610.12375v1 Announce Type: new Abstract: Agents are deployed in applications from trip planners and stock trading to IT incident triage. In most cases, LLM agents work autonomously with minimal

Open-ended Scientific Discovery with Possibilistic Reasoning

arXiv:2610.11289v1 Announce Type: new Abstract: Autonomous scientific discovery with LLMs requires generating and testing hypotheses adaptively as evidence accumulates while maintaining statistical va

Local Ai

Open-Vocabulary Audio-Visual Event Localization via Complex-Valued Fusion

arXiv:2610.11846v1 Announce Type: new Abstract: Open-Vocabulary Audio-Visual Event Localization (OV-AVEL) labels each video segment with an event class, including classes that were never seen during t

local-aiarxiv-cs-cv

OpenProblemBench: Benchmarking AI on Open Problems in the Foundational Theoretical Sciences

arXiv:2610.11118v1 Announce Type: new Abstract: The next frontier for artificial general intelligence is tackling unresolved scientific problems, calling for benchmarks that assess progress beyond est

Opera: A Verbal Critic Framework for Long-horizon Coding Agents

arXiv:2609.33987v2 Announce Type: replace Abstract: Long-horizon coding agents need timely corrections, yet feedback can be ineffective or even harmful when it misjudges ongoing work or fails to addre

safetyarxiv-cs-cl

Optimal random quantisers for spherically symmetric distributions

arXiv:2610.11772v1 Announce Type: cross Abstract: Zador's celebrated theorem is a cornerstone of optimal quantisation: it establishes both the weak limit of the empirical distribution of an optimal n-

Optimal Transport Depth Up-Scaling

arXiv:2508.08011v2 Announce Type: replace Abstract: Pre-training Large Language Models (LLMs) from scratch at larger scales yields remarkable performance but incurs substantially high training costs.

researcharxiv-cs-cl

Optimally Pacing Budget Spending and Learning

arXiv:2610.11074v1 Announce Type: new Abstract: We establish near-optimal regret bounds for budget-constrained online learning against arbitrary classes of budget-pacing experts in the adversarial set

Optimizing Large Language Models with Chained LMOs

arXiv:2610.10975v1 Announce Type: cross Abstract: Muon has motivated a growing family of optimizers that compose multiple matrix normalizations, but these methods remain fragmented and lack a unified

ORDO: Operation-level Round-aware Dynamic Ordering for MIP Presolve

arXiv:2610.11294v1 Announce Type: new Abstract: Presolve strongly affects mixed-integer programming (MIP) performance, yet learning-based methods only optimize parameter configurations and cannot expr

OuroWorld: Bringing Any 3D World Alive as Diverse, Endlessly Looping 3D Cinemagraphs

arXiv:2610.12461v1 Announce Type: new Abstract: Recent 3D world models generate photorealistic, explorable scenes that remain frozen in time. OuroWorld is a mask-free framework that turns any static 3

tutorialsarxiv-cs-cv

Overcoming Prior Barriers: Supervised Fine-Tuning under Long-Tail Distribution

arXiv:2610.12345v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) adapts pretrained large language models (LLMs) to downstream tasks, but the required concepts can receive substantially dif

OX-NeRF: 3D X-ray Tomography Reconstruction from Sparse Views Using Implicit Neural Representation

arXiv:2610.11547v1 Announce Type: new Abstract: NeRF and Gaussian splatting methods have been successfully applied on X-ray scenes where the views are too sparse for 3D reconstruction via classical me

researcharxiv-cs-cv

Hardware

PageWeaver: KV-Guided Query Unions for Sparse Attention

arXiv:2610.11201v1 Announce Type: cross Abstract: Dynamic sparse attention limits the KV pages selected by each query, but a small support does not necessarily yield efficient GPU work. Query unions s

hardwarearxiv-cs-lg

PAM-ToD: Plug-and-Play Appearance Modeling for Cross-Time-of-Day 3D Gaussian Splatting

arXiv:2610.11572v1 Announce Type: new Abstract: Adapting a pre-trained 3D Gaussian Splatting (3DGS) road scene to a new time of day requires learning appearance changes from a few anchor images while

Parametric Trajectory Distillation for Few-Step Video Generation

arXiv:2610.11498v1 Announce Type: new Abstract: Video diffusion and flow models require many sequential evaluations, making generation computationally expensive. Few-step distillation reduces this cos

tutorialsarxiv-cs-cv

PaReGTA: A Temporally Aware LLM-Based Patient Representation Framework for EHR Analytics

arXiv:2602.19661v3 Announce Type: replace Abstract: Temporal information in structured electronic health records (EHRs) is often lost in sparse one-hot or count-based representations, while sequence m

PathLang: A Language-Centered Benchmark for Vision-Language Models in Computational Pathology

arXiv:2610.11329v1 Announce Type: new Abstract: Pathology vision-language models (VLMs) have shown strong visual perception ability, but their robustness in the language domain remains poorly characte