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Non-Euclidean Gradient Descent Has Many Edges of Stability

arXiv:2610.15223v1 Announce Type: cross Abstract: Recent observations that deep learning models train at the edge of stability have renewed interest in understanding stability properties of classical

Applications

Nonparametric Kernel Clustering with Bandit Feedback

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DGX agent

arXiv:2601.07535v2 Announce Type: replace-cross Abstract: Clustering with bandit feedback refers to the problem of partitioning the arms of an N-armed stochastic bandit according to their underlying d

applicationsarxiv-cs-lg

14 Oct 2026

Not All Challenges Are Equal: Evaluating Interactive Liveness Detection Against Real-Time Face Swaps

arXiv:2610.16417v1 Announce Type: new Abstract: Remote identity verification assumes that a camera stream shows a live person, yet public real-time face-swap tools can replace that person before the s

researcharxiv-cs-cv

Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs

arXiv:2610.18412v1 Announce Type: new Abstract: Prompt-based adaptation offers several ways to specialize a frozen language model, ranging from natural-language prompt optimization to learned continuo

Not All Features Are Created Equal: A Mechanistic Study of Vision-Language-Action Models

arXiv:2603.19233v2 Announce Type: replace Abstract: A fine-tuned Vision-Language-Action (VLA) policy can easily place the alphabet soup in the basket on command, just as it saw during fine-tuning. How

Not Every Cue Is Evidence: Cue Supportability for Forensic Distillation in Face Forgery Detection

arXiv:2610.16452v1 Announce Type: new Abstract: Explainable face forgery detection requires not only correct judgments but also forensic evidence supported by the input image. However, training cues a

researcharxiv-cs-cv

Notes on Uniform Policy Score Bounds and Lipschitz Continuity for Truncated Gaussian, Product Cauchy, Boltzmann, and Softmax Policies

arXiv:2610.15690v1 Announce Type: cross Abstract: We establish uniform score bounds and Lipschitz continuity for truncated Gaussian, Cauchy, Boltzmann, and softmax policies under explicitly stated par

Novel View Synthesis using DDIM Inversion

arXiv:2508.10688v3 Announce Type: replace Abstract: Single-image novel view synthesis requires viewpoint-dependent structural changes while retaining source-scene information and inferring unobserved

researcharxiv-cs-cv

NVIDIA Nemotron Bolt 3.5 74B A9B releases Oct. 15th

model-releasesr-localllama

This model is a 74B parameter, 9B-active-parameter mixture-of-experts (MoE) model, packed into 24GB using 2-bit quantization with advanced quantization-aware training techniques to retain quality. It

OATH: Object-Agnostic Tracking for Dexterous Hands, Enabling Rapid Physics-based Retargeting

arXiv:2610.14379v1 Announce Type: new Abstract: Retargeting human demonstrations into dynamically feasible dexterous hand trajectories requires resolving complex contact physics, yet current methods e

Observation Quality in DSM-Assisted Aerial Localization: A Benchmark and Base-Anchored Uncertainty Calibration

model-releasesarxiv-cs-cv

arXiv:2610.18564v1 Announce Type: new Abstract: Aerial map matching provides position observations for navigation, but a position error alone does not determine how an observation should be weighted.

Agents

Observation-Referenced Tissue Deformation Forecasting with Tool-Driven and Residual Dynamics

arXiv:2610.14191v1 Announce Type: new Abstract: Forecasting tissue deformation from future instrument motion could support predictive planning and increasingly autonomous surgical systems. We study th

agentsarxiv-cs-ro

Observe, Share, or Predict? Disentangling Partner Information in Embodied Multi-Agent Collaboration

model-releasesarxiv-cs-ro

arXiv:2610.16895v1 Announce Type: cross Abstract: LLMs are used as high-level controllers for embodied agents, but it remains unclear whether they support collaborative reasoning, such as anticipating

ObsSpec: Accelerating Agentic Reinforcement Learning with Observation Speculation

arXiv:2610.15246v1 Announce Type: new Abstract: Tool-using agents alternate between model generation and tool execution, waiting for each tool call to complete before starting the next generation. Thi

Agents

OccWAM: Occupancy World-Action Models with Joint Scene-to-Ego Flow Matching

arXiv:2610.17688v1 Announce Type: new Abstract: Occupancy world models provide a structured, metric representation of future driving scenes, making them a promising basis for end-to-end autonomous dri

agentsarxiv-cs-ro

OceanFlow: Coherent Flow Matching for Probabilistic Ocean Modeling

arXiv:2610.15518v1 Announce Type: new Abstract: Probabilistic ocean modeling is essential for risk-aware marine operations, climate analysis, and scientific decision-making, where downstream decisions

OctMole: Multiscale 3D Molecular Generation with Hierarchical Spatial Flows

arXiv:2610.16586v1 Announce Type: new Abstract: Generating accurate 3D molecules requires jointly determining a variable number of atoms, their identities, and geometry across spatial scales: global s

Offensive security startup CodeWall launches with $4M for AI agents that attack enterprises

model-releasessiliconangle

London-based offensive security startup CodeWall Ltd. officially launched today with 4 million in new funding to give enterprises such as banks autonomous artificial intelligence agents that attack th

OIL: An Object Interaction Layer for Humanoid Whole-Body Control

arXiv:2610.14033v1 Announce Type: new Abstract: Recent progress in learned whole-body controllers for humanoid robots has enabled robust free-space motion by tracking kinematic targets such as joint o

Omni-Modal LLMs Know More Than They Say

arXiv:2610.16135v1 Announce Type: cross Abstract: Omni-modal large language models (Omni-LLMs) extend LLMs to process heterogeneous modalities (e.g., vision, audio, and IMU signals) alongside language

safetyarxiv-cs-cv

Tutorials

Omni-RS: Unifying Multi-Task Understanding and Generation in Remote Sensing

arXiv:2610.18323v1 Announce Type: new Abstract: Practical remote sensing applications often require both understanding and generation capabilities for the same geographic scene. However, most existing

tutorialsarxiv-cs-cv

OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understanding

model-releasesarxiv-cs-cv

arXiv:2610.18829v1 Announce Type: new Abstract: With the rapid development of multimodal large language models (MLLMs), evaluating their video understanding capabilities has become increasingly import

OmniRaindrop: A Comprehensive Benchmark for Dual-Focused Raindrop Removal under Day and Night Scenes

model-releasesarxiv-cs-cv

arXiv:2510.17719v3 Announce Type: replace Abstract: Adherent raindrops on camera lenses are a pervasive source of real-world image degradation, undermining outdoor visual perception for downstream app

OmniRSI: Taming Latent Diffusion for Unified Multimodal Remote Sensing Image Generation

arXiv:2610.15414v1 Announce Type: new Abstract: Multimodal remote sensing image generation emerges as a promising paradigm for recovering complete and complementary observations from heterogeneous and

safetyarxiv-cs-cv

On Collusion-Free Teaching in Planar Graphs

arXiv:2610.18714v1 Announce Type: cross Abstract: Non-clashing teaching minimizes teaching complexity among models satisfying the Goldman--Mathias collusion-avoidance criterion. While the Vapnik--Cher

On Generalizing Preference-based Reinforcement Learning to Multi-Objective Problems

arXiv:2607.11432v2 Announce Type: replace Abstract: We study reinforcement learning (RL) from pairwise human-generated trajectory comparisons. We generalize preference-based RL by formalizing a novel

On Making SE(2)-Invariant Networks Optimal

arXiv:2610.17424v1 Announce Type: new Abstract: Respecting a-priori known symmetries of underlying data is a key principle in designing efficient neural architectures. Convolutional neural networks ar

On physical implementability of reinforcement-learning-driven reasoning

arXiv:2610.14131v1 Announce Type: cross Abstract: In the machine learning community, the reinforcement-learning-driven reasoning process has been intensively studied since around 2025. In this article

On Sleep-Wake Disorder Identification from Polysomnographic Representations

arXiv:2610.17451v1 Announce Type: new Abstract: Self-supervised polysomnography foundation models (PSG FMs) aim to capture clinically useful information beyond sleep staging, yet are rarely compared w

On the Calibration of Crowdsourcing Aggregators

arXiv:2610.16815v1 Announce Type: cross Abstract: Calibration has been extensively studied for classifiers, but remains largely unexplored for crowdsourcing aggregation methods, which are increasingly

On the Depth Transfer of Neural Optimizers via L-Smoothness

arXiv:2610.14344v1 Announce Type: new Abstract: Scaling deep learning models efficiently requires predictable performance transfer across compute regimes. Establishing reliable hyperparameter transfer

On the Dynamics of Delayed Generalization in Linear Models

arXiv:2602.08302v2 Announce Type: replace Abstract: Delayed generalization, where training accuracy becomes nearly perfect substantially before test accuracy, has often been studied in the context of

On the Monitorability of Natural Language Autoencoders: Identification vs Discrimination

arXiv:2610.18727v1 Announce Type: new Abstract: Natural Language Autoencoders (NLAs) are proposed as oversight tools for language models: an Activation Verbaliser (AV) writes an English explanation of

On the Optimality of Random Features under General Conditions: Misspecified Cases and Robust Learning

arXiv:2610.18333v1 Announce Type: cross Abstract: Random features provide a standard approach to scaling kernel methods to large datasets by approximating the kernel with a finite-dimensional feature

On the Statistical Mechanics of Attention

arXiv:2610.17347v1 Announce Type: new Abstract: We revisit the statistical mechanics of Transformer attention via modern Hopfield models. We present a free-energy formalism to characterize parameter r

On the Theoretical Limits of Learning Unconstrained Interatomic Potentials

arXiv:2610.15078v1 Announce Type: new Abstract: Machine-learned interatomic potentials (MLIPs) accelerate molecular dynamics (MD) simulations by learning the energies and forces that govern atomic mot

Applications

On the Upper Bounds for the Matrix Spectral Norm

arXiv:2506.15660v2 Announce Type: replace-cross Abstract: We consider the problem of estimating the spectral norm of a matrix using only matrix-vector products. We propose a new Counterbalance estimat

applicationsarxiv-cs-lg

Tutorials

One-Layer Transformers Provably Learn In-Context K-Nearest Neighbor Prediction with Chain-of-Thought

arXiv:2610.16798v1 Announce Type: new Abstract: Chain-of-Thought (CoT) enables transformers to solve complex reasoning tasks by generating intermediate reasoning steps. Despite strong empirical succes

tutorialsarxiv-cs-lg

One Model, Multiple Compute Budgets: Multi-Ratio ConceptMoE for Flexible Compute Across Turns

arXiv:2610.14074v1 Announce Type: new Abstract: Large language models typically support different inference budgets through separate model sizes or fixed compute configurations. In multi-turn interact

One-Shot Learning for Double-Shot Latte Art: Adapting Cobot Pouring Skills with Dynamic Movement Primitives

arXiv:2610.16179v1 Announce Type: new Abstract: Latte art is a complex task requiring dexterity and precision, making it a challenging and commercially relevant task. A one-shot learning system is emp

researcharxiv-cs-ro

One Size Does Not Fit All: Distribution-Guided Tensor-Wise Adaptive Sparsification for Model Merging

arXiv:2508.06163v2 Announce Type: replace-cross Abstract: Merging multiple task-specific fine-tuned checkpoints into a single multi-task model provides an attractive alternative to joint multi-task tr

One-Way Coupling via Patchwise Adaptive Normalization in Transformer Surrogates

arXiv:2610.14687v1 Announce Type: new Abstract: Many physical systems exhibit one-way coupling: exogenous fields such as medium density or obstacle geometry influence the dynamics without being affect

OneDemo: Unified Policy Learning and Data Generation from a Single Demonstration

arXiv:2610.18188v1 Announce Type: new Abstract: Learning robot policies from demonstrations is often limited by the cost of collecting diverse, high-quality expert data. While an appealing alternative

OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection

arXiv:2610.16219v1 Announce Type: cross Abstract: Time series (TS) anomaly detection (AD) lacks a universal detector, with the best choice determined by the series rather than by the algorithm. Existi

Online Individualized Treatment Selection under Partial Feedback: False Discovery Control and Minimax Optimality

arXiv:2610.16464v1 Announce Type: cross Abstract: We study online individualized treatment selection when only the outcome under the assigned treatment is observed. Our objective is to preferentially

Online learning of neural state-space models

arXiv:2607.17614v2 Announce Type: replace-cross Abstract: Recent advances in deep-learning-based nonlinear system identification have led to encoder-based estimation of neural state-space (ANN-SS) mod

Online LLM Personalization from Pairwise Preferences Across Users

arXiv:2610.17203v1 Announce Type: new Abstract: Large language models can answer the same question in different styles, but users' preferred styles are hidden and vary across users and questions. We s

Only What You've Signed up For: Secure Workflows with Constrained Diffusion Agents

arXiv:2610.15177v1 Announce Type: new Abstract: As agentic AI systems take on increasingly complex and consequential tasks, securing them against prompt injection becomes critical. Existing defenses o