Search: “papers”

Learnable Low-Rank Polynomial Sketch for Effective Linear Attention

arXiv:2610.14219v1 Announce Type: new Abstract: Softmax attention in Transformers suffers from quadratic complexity in sequence length, making it impractical for long-context applications. Linear atte

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Learning Graded Sparse Mechanisms: A Representation Theorem and Certified Generalization

DGX agent

arXiv:2610.16537v1 Announce Type: new Abstract: In a variety of multi-agent and physical environments, an element's update depends strongly on a few other elements and weakly on many, and both the ide

agentsarxiv-cs-lg

14 Oct 2026

Learning Safety-Constrained Whole-Body Loco-Manipulation for Lunar Surface Operations

arXiv:2610.15486v1 Announce Type: new Abstract: Robotic systems for lunar surface operations must act autonomously and preserve their own integrity in an environment where failures are unrecoverable a

Learning to Assist: Collaborative VLAs for Implicit Human-Robot Collaboration

arXiv:2606.12475v2 Announce Type: replace Abstract: Human-robot collaboration (HRC) combines the complementary strengths of humans and robots to improve task efficiency. However, many existing collabo

Learning to Design Convex Relaxations for Nonconvex Optimization

arXiv:2610.16948v1 Announce Type: new Abstract: Convex relaxations make nonconvex optimization tractable by replacing hard nonconvex problems with convex surrogates that give valid bounds. Yet their d

LiDAR-based Humanoid Navigation in Dynamic, Non-Convex Environments via Spatio-Temporal Safe Corridors

arXiv:2610.17232v1 Announce Type: new Abstract: Navigating humanoid robots through dynamic, non-convex environments requires tightly coupling onboard perception with footstep planning and dynamic loco

researcharxiv-cs-ro

Lifting Finite-Sample Gaussian Approximation of Wide Neural Networks to Functional Convergence

arXiv:2610.18166v1 Announce Type: cross Abstract: Randomly initialised neural networks of width m converge to Gaussian processes as m o infty, with two types of quantitative bounds characterising this

LiFUSE: Frequency-Spatial Unified Self-Supervised Pretraining for Liver Disease Analysis on Non-contrast CT

arXiv:2610.18243v1 Announce Type: new Abstract: Given the growing global burden of liver tumors and diffuse liver diseases, non-contrast computed tomography (NCCT) is increasingly used for opportunist

safetyarxiv-cs-cv

Applications

LLM Evaluation is a Forecasting Problem

arXiv:2610.17018v1 Announce Type: new Abstract: Standard procedure in machine learning is to randomly split train and test data, emulating IID draws from one underlying data distribution. In practice,

applicationsarxiv-cs-lg

Localized Neural learning vs backpropagation [D]

researchr-machinelearning

I’ve recently started exploring machine learning by building neural networks from scratch and studying the underlying mathematics. I come from a physics and mathematics background, and as I’ve become

Hardware

LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs

arXiv:2610.18393v1 Announce Type: cross Abstract: Equivariant machine learning interatomic potentials (MLIPs) promise atomistic simulations with accuracy approaching that of quantum-mechanical calcula

hardwarearxiv-cs-lg

MFVSR: A Multi-Style and Fine-Grained Benchmark for Video Subtitle Removal

model-releasesarxiv-cs-cv

arXiv:2610.18461v1 Announce Type: new Abstract: Video subtitle removal (VSR) is an important video restoration task, yet existing evaluation remains limited by low-resolution datasets, fragmented test

Natural Language Generation from Visual Events: State-of-the-Art and Key Open Questions

arXiv:2502.13034v4 Announce Type: replace-cross Abstract: In recent years, a substantial body of work in visually grounded natural language processing has focused on real-life multimodal scenarios suc

NetRL: Powering Network Decisions via NetGRPO-Driven Reinforcement Learning

arXiv:2610.16226v1 Announce Type: cross Abstract: Network decision-making tasks, including adaptive bitrate streaming, load balancing, and congestion control, require policies to process heterogeneous

safetyarxiv-cs-lg

Neural Bayesian Networks for Modular and Scalable Decision-Making Under Uncertainty

arXiv:2610.16902v1 Announce Type: cross Abstract: Bayesian Networks (BNs) are established tools for letting agents reason under uncertainty in sequential decision-making. They split a joint distributi

Neural-Surrogate Co-Design of Aerodynamic Shape and Control

arXiv:2603.06760v2 Announce Type: replace Abstract: Control co-design jointly optimizes a robot's physical design and its controller. Existing techniques are limited in the number of model parameters

researcharxiv-cs-ro

Noise2Match: Self-Supervised Low-Dose CT Denoising via Adaptive Inter-Slice Matching

arXiv:2610.18379v1 Announce Type: new Abstract: With the rapid development of deep learning, supervised convolutional neural network methods have greatly improved the performance of low-dose computed

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 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

safetyarxiv-cs-lg

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

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 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

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

safetyarxiv-cs-lg

Tutorials

OPTIC-ER: A Reinforcement Learning Framework for Real-Time Emergency Response and Equitable Resource Allocation in Underserved African Communities

arXiv:2508.12943v3 Announce Type: replace-cross Abstract: Public service systems in many African regions suffer from delayed emergency response and spatial inequity, causing avoidable suffering. This

tutorialsarxiv-cs-lg

PA-LOCO: Learning Perturbation-Adaptive Locomotion for Quadruped Robots

arXiv:2407.04224v2 Announce Type: replace Abstract: Numerous locomotion controllers have been designed based on Reinforcement Learning (RL) to facilitate blind quadrupedal locomotion traversing challe

PACT: Posture-Aligned Co-Design Technique for Hand Configuration and Retargeting in Multi-Finger Imitation Learning

arXiv:2610.18443v1 Announce Type: new Abstract: The fidelity of teleoperated demonstrations used for imitation learning is determined by how faithfully a robot hand can replicate the operator's postur

Perception Beyond the Visible: Incompleteness-Aware Scene Understanding for Robot Autonomy

arXiv:2610.18670v1 Announce Type: cross Abstract: Reliable robot autonomy requires reasoning about the world from partial and noisy observations. However, many perception systems model only visible sc

PhaseQuant: Mitigating Activation Quantization Collapse via Residual-Stream Dynamics

arXiv:2606.12487v2 Announce Type: replace Abstract: Post-training quantization (PTQ) is essential for efficient large language model (LLM) inference, but reliably quantizing activations remains challe

Physical SLAM: Physics-aware Simultaneous Localisation and Mapping

model-releasesarxiv-cs-ro

arXiv:2610.17260v1 Announce Type: new Abstract: To reconstruct a predictive 4D representation of the physical world from monocular video captured by a moving camera, we jointly reconstruct dynamic Gau

Physics-Informed State Space Models for Day-Ahead Photovoltaic Power Forecasting

arXiv:2610.14014v1 Announce Type: new Abstract: Accurate day-ahead photovoltaic (PV) power forecasting requires both strong temporal modeling and adherence to physical PV system constraints. Purely da

PLDiv: Measuring Dataset Diversity and Semantic Collapse via Persistence Landscapes

arXiv:2610.14031v1 Announce Type: cross Abstract: Modern data pipelines increasingly combine human-created and human-curated data with AI-generated data for model training and evaluation. As datasets

PointWalker: Mapless Quadruped Locomotion Directly From Raw LiDAR Point Clouds

arXiv:2610.15548v1 Announce Type: new Abstract: Quadruped robots deployed for inspection and field missions must cross heterogeneous terrain, from stairs, slopes, and gaps to high platforms, stepping

Tutorials

Posterior Flow Matching

arXiv:2610.13994v1 Announce Type: cross Abstract: Flow matching learns how to transport noise toward data, but its mean prediction does not directly report the endpoint uncertainty remaining at an int

tutorialsarxiv-cs-cv

Predict Before You Modify: Learning Improvement Dynamics for Self-Evolving Agents

arXiv:2610.13991v1 Announce Type: new Abstract: Self-evolving agents improve through repeated cycles of modification and evaluation. However, existing methods receive feedback on modifications only af

agentsarxiv-cs-lg

PRIME: Frequency-Aware Unified Pruning and Merging for MoE Compression

arXiv:2610.13982v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models have emerged as an effective paradigm for scaling large language models, yet their massive parameter footprint poses sev

Probabilistic Multi-Source Data Selection via Learnable Categorical Priors: Variance Reduction and Guaranteed Derandomization

arXiv:2610.14942v1 Announce Type: new Abstract: Bilevel data selection is a fundamental task with numerous applications in machine learning and statistics. A common strategy for tackling this problem

ProML: Progressive Multi-label Learning

arXiv:2610.15614v1 Announce Type: new Abstract: Multi-label learning typically pursues model construction through the joint optimization of label fitting, correlation modeling, and structural regulari

Prorsa: Unleashing the Potential of Prefill-Decode Disaggregation in Agentic Reinforcement Learning

arXiv:2610.16592v1 Announce Type: cross Abstract: Agentic reinforcement learning (RL) is commonly used to enhance the capabilities of large language models (LLMs) in solving complex multi-turn tasks.

agentsarxiv-cs-lg

Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images

arXiv:2610.15286v1 Announce Type: new Abstract: The accuracy of detecting protein crystals for fluorescence microscopy images is very critical for high throughput and automated systems. Although the t

PVFH-Bench: Benchmarking Premise-Induced Visual Fixation Hallucinations in Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2610.14635v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable progress across a wide range of multimodal tasks. However, object hallucination remains

Random Feature Pruning for Multi-Class Classification

arXiv:2610.15101v1 Announce Type: new Abstract: Weighted sums of random kitchen sinks (W-RKS) is a commonly used feature extraction technique for supervised learning tasks. A major drawback of this me

Recursive self-improvement is one of the most discussed ideas in AI right now. RSI-Anything measures it in code repair, GUI agents, lab plan…

Recursive self-improvement is one of the most discussed ideas in AI right now. RSI-Anything measures it in code repair, GUI agents, lab planning, and chemistry. They find that the lab-planning score m

safetydair-ai--x

Reference-Efficient Multimodal Bitstream-Corrupted Video Recovery via Depth-Conditioned Adaptation

model-releasesarxiv-cs-cv

arXiv:2610.15806v1 Announce Type: new Abstract: Recovering visually faithful content from damaged video bitstreams is important for reliable multimedia communication and storage. Existing methods expl

Removing the Compute Bottleneck for Event-based Visual Place Recognition with Spiking Neural Networks

arXiv:2610.16022v1 Announce Type: new Abstract: Neuromorphic sensing and computing promise low latency and high energy efficiency for robotics, yet most event-based visual place recognition (VPR) pipe

RePrior: Measurement-Reconciled Prior Correction for Sparse-View CBCT Reconstruction

arXiv:2610.14102v1 Announce Type: cross Abstract: Sparse-view cone-beam computed tomography (CBCT) is severely under-constrained by limited X-ray projections. Learned reconstruction can provide comple

safetyarxiv-cs-cv

Local Ai

Requests of a Feather Must Flock Together: Batch Size vs. Prefix Homogeneity in LLM Inference

arXiv:2605.06046v2 Announce Type: replace Abstract: Auto-regressive token generation in large language models is memory-bound because it requires 'attending to' key and value tensors (KV cache) of all

local-aiarxiv-cs-lg

ResearchFlow-Eval: Tracing the Execution Gap in End-to-End Multi-Claim Autoresearch

arXiv:2610.18088v1 Announce Type: cross Abstract: Autoresearch systems increasingly undertake entire research projects. In practice, scientific research requires systems to pursue multiple interdepend

agentsarxiv-cs-lg

Rethinking Opinion Measurement Interfaces for Human-in-the-Loop Optimization

arXiv:2610.14757v1 Announce Type: cross Abstract: Human-in-the-loop optimization refers to systems that present choices to a human decision-maker and query their judgment to optimize system parameters