Search: “papers”

Retrievable Gradients: Continual Post-Training Without Cumulative Weight Drift

arXiv:2606.15734v2 Announce Type: replace-cross Abstract: Continual post-training enables models to absorb emerging knowledge after deployment, but repeatedly updating shared parameters can accumulate

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Revisited Support Vector Machines: A Unified Joint Optimization Framework with Nystrom Approximation for Scalable Adaptive Learning

DGX agent

arXiv:2610.15498v1 Announce Type: new Abstract: The theoretical elegance of Support Vector Machines (SVMs), grounded in statistical learning theory, establishes them as a reference for robust classifi

applicationsarxiv-cs-lg

14 Oct 2026

Revisiting Natural Language Prompts from Large Language Models for Open-Vocabulary Video Recognition

arXiv:2610.16666v1 Announce Type: new Abstract: Vision-language models (VLMs) like CLIP have shown great success in image tasks, however, applying CLIP to open-vocabulary video recognition poses chall

RIECNN: Real-Time Image Enhanced CNN for Traffic Sign Recognition

model-releasesarxiv-cs-cv

arXiv:2610.15127v1 Announce Type: new Abstract: Traffic sign recognition plays a crucial role in the development of autonomous cars to reduce the accident rate and promote road safety. It has been a n

RoboFake: Are Deepfake Detectors Ready for Humanoid Face Robots?

arXiv:2610.14269v1 Announce Type: new Abstract: Humanoid face robots can now move their lips in sync with speech and show detailed facial expressions. Give such a robot the face of a real person and d

RoboFQE: Rollout-Free Evaluation of Visual Robot Policies from Offline Trajectories

arXiv:2610.18699v1 Announce Type: new Abstract: Evaluating robot policies before deployment typically requires producing new trajectories through real-world execution, simulators, or learned world mod

Safety-Oriented Trajectory Ranking from BEV Perception Outputs

arXiv:2610.14066v1 Announce Type: cross Abstract: Bird's-Eye-View (BEV) representations are increasingly used as compact inputs for autonomous driving perception and planning. However, lightweight and

Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation

arXiv:2409.12333v2 Announce Type: replace-cross Abstract: Extracting hepatic vessels from abdominal images is of high interest for clinicians since it allows to divide the liver into functionally-inde

Scenes as an Attack Surface: Uncovering Scene-Grounded Safety Failures in MLLMs

arXiv:2610.14094v1 Announce Type: new Abstract: Existing jailbreak attacks have exposed serious safety weaknesses in multimodal large language models (MLLMs). Most prior attacks rely on specially cons

SelectTTRL: Representation-Guided Data Selection for Efficient Test-Time Reinforcement Learning

arXiv:2610.16104v1 Announce Type: new Abstract: Test-Time Reinforcement Learning (TTRL) enables large language models to adapt directly on unlabeled test problems by constructing pseudo-labels from mu

Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images

arXiv:1307.2965v3 Announce Type: replace Abstract: The automatic segmentation of human knee cartilage from 3D MR images is a useful yet challenging task due to the thin sheet structure of the cartilag

Semantic Edge-Cloud Communication for Real-Time Urban Traffic Digital Twins

arXiv:2610.14187v1 Announce Type: cross Abstract: Urban traffic systems are becoming increasingly complex, demanding real-time traffic perception, proactive collision prediction, and intelligent manag

SERF: Spatiotemporal Environment and Robot Feature Map for Long-Horizon Mobile Manipulation

model-releasesarxiv-cs-ro

arXiv:2606.12956v2 Announce Type: replace Abstract: Long-horizon robot mobile manipulation requires continual reasoning about localization, environment changes, and task progress, all of which are cha

SHRUMS: Sensor Hallucination for Real-time Underwater Motion Planning with a Compact 3D Sonar

arXiv:2510.18996v2 Announce Type: replace Abstract: Autonomous navigation in 3D is a fundamental problem for autonomy. Despite major advancements in terrestrial and aerial settings due to improved ran

agentsarxiv-cs-ro

SIGN-MAPPO: State-Intent-Aware Graph Multi-Agent Reinforcement Learning for Cooperative Driving under Imperfect V2X

arXiv:2610.15577v1 Announce Type: new Abstract: Cooperative driving at mixed-traffic unsignalized intersections is challenged by imperfect vehicle-to-everything (V2X) communication, human-driven vehic

Silence and Noise: Resolving Suppression and Overvaluation in Multimodal Entity Alignment

arXiv:2610.16106v1 Announce Type: new Abstract: Multimodal entity alignment (MMEA) is fundamental to integrating heterogeneous knowledge graphs (KGs), yet existing paradigms remain critically vulnerab

Soft Teacher Forcing

arXiv:2610.17189v1 Announce Type: cross Abstract: Soft tokens have so far been used to replace discrete chain-of-thought in latent reasoning methods. These approaches typically require multi-stage tra

SoK: Rethinking Membership Inference in Tabular Machine Learning

arXiv:2610.18486v1 Announce Type: new Abstract: Membership Inference Attacks (MIAs) are currently the dominant approach to evaluating privacy in machine learning applications. Despite their significan

SPARCL: Spatially Adaptive Regional Coverage for Active Learning in Chest X-ray Analysis

arXiv:2610.17808v1 Announce Type: new Abstract: Pixel-level annotation for chest X-ray (CXR) lesion segmentation is expensive, making active learning (AL) essential. However, existing AL strategies fa

SpikeOcc: Towards Resilient Multimodal 3D Occupancy Perception via Spiking Experts

arXiv:2610.15745v1 Announce Type: new Abstract: Multimodal 3D occupancy prediction underpins safe autonomous driving through comprehensive scene understanding. However, existing methods have largely p

agentsarxiv-cs-cv

Applications

Split Conformal Interval Length under Huber Contamination

arXiv:2610.15458v1 Announce Type: new Abstract: Existing analyses of interval length in split conformal prediction largely focus on clean data. However, how contamination at the training and calibrati

applicationsarxiv-cs-lg

SPROUT: Scaling Agentic Reinforcement Learning for Long-Horizon Software Engineering

arXiv:2610.14405v1 Announce Type: cross Abstract: Reinforcement learning (RL) has driven substantial progress in coding agents by relying on verified successful trajectories as learning signals. Howev

Applications

STREGO: A Scalable Implementation of a Trust-Region Framework for Noisy Bayesian Optimization

arXiv:2610.14338v1 Announce Type: cross Abstract: TREGO, a trust-region Bayesian optimization (BO) method for expensive blackbox problems, has convergence guarantees, but only for exact function value

applicationsarxiv-cs-lg

Sublinear Algorithms for Wasserstein and Total Variation Distances: Applications to Fairness and Privacy Auditing

arXiv:2503.07775v3 Announce Type: replace Abstract: Resource-efficiently computing representations of probability distributions and the distances between them while only having access to the samples i

SurgiCLAW: An Interpretable Atomic-Skill Agent for Precision Robotic Micromanipulation

arXiv:2610.15766v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) and vision--language--action models have provided robots with new capabilities for long-horizon task und

Targeting the Right Situations: Mutation Testing of Fuzzy-Powered Situation Hyperspace

arXiv:2610.15122v1 Announce Type: new Abstract: Ensuring the safety of autonomous Unmanned Aerial Vehicles (UAVs) operating in underground mines is challenging due to the vast and uncertain Operationa

Text-Guided Medical Image Segmentation via Residual Aggregation and Dense Alignment Representations

model-releasesarxiv-cs-cv

arXiv:2610.14944v1 Announce Type: new Abstract: Specifying segmentation targets with natural language provides flexible semantic conditioning for medical image segmentation. However, existing methods

The Bell Boundary of Shared Representations

arXiv:2610.16828v1 Announce Type: new Abstract: When does a shared representation become insufficient for jointly supporting multiple tasks? This question lies at the heart of multi-task learning mode

The Tokens Strike Back: Safety-Aware Denoising with Directional Embedding-based Revision

arXiv:2610.17636v1 Announce Type: new Abstract: The Safety-Aware Denoiser (SAD) is a recent, training-free, safety-guidance framework for text diffusion models. During inference, SAD steers text sampl

Tutorials

Toward Agents That Reason About Their Computation

arXiv:2510.22833v2 Announce Type: replace-cross Abstract: While reinforcement learning agents can achieve superhuman performance in many complex tasks, they typically do not become more computationall

tutorialsarxiv-cs-lg

Tutorials

Towards Modality-Agnostic Seizure Classification: A Unified Deep Learning Framework for Heterogeneous EEG and Intracranial EEG Signals

arXiv:2610.16596v1 Announce Type: new Abstract: Seizure prediction and detection, essential for clinical treatment of epilepsy, remain the crucial task of this neurological disease. EEG and iEEG are w

tutorialsarxiv-cs-lg

Traditional and Recent Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 1)

arXiv:2610.18276v1 Announce Type: new Abstract: Infrared small target detection is often used in different civilian and military applications to detect static or moving targets in scenes like in groun

Training-Free Multi-Agent Cooperative Embodied Search from Uncertain Vision-Language Priors

arXiv:2610.15038v1 Announce Type: new Abstract: Real-world Vision-Language Navigation (VLN) requires agents to act under partial observability, where map structure, semantic cues, and target locations

Tube-Based Robust Control Strategy for Vision-Guided Autonomous Vehicles

arXiv:2503.18752v3 Announce Type: replace-cross Abstract: A robust control strategy for autonomous vehicles can improve system stability, enhance riding comfort, and prevent driving accidents. This pa

agentsarxiv-cs-cv

Understanding and Mitigating Training Instability in Large-Scale Recommender Systems: A Cascade Perspective

arXiv:2610.14394v1 Announce Type: cross Abstract: Training large-scale recommender systems on dynamic industrial platforms, such as Short-Form Video (SFV) services with billions of daily interactions,

Unsupervised and Supervised Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 2)

arXiv:2610.18291v1 Announce Type: new Abstract: Since the rise of supervised deep learning modelet dets, researchers has attempt to solve infrared small targection using various models like convention

Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization

model-releasesarxiv-cs-cv

arXiv:2204.09334v4 Announce Type: replace-cross Abstract: Unsupervised domain adaptation approaches have recently succeeded in various medical image segmentation tasks. The reported works often tackle

VLCQ: Post-training quantization for deep neural networks using variable length coding

arXiv:2610.15092v1 Announce Type: new Abstract: Quantization plays a crucial role in efficiently deploying deep learning models on resource-constrained devices. Post-training quantization does not req

What Can Language Models Inherit from Solomonoff Induction? Limits, Attainable Properties, and What Served Conditionals Reveal

arXiv:2610.14007v1 Announce Type: new Abstract: A familiar line of reasoning runs as follows: language models are flexible approximators trained under a strictly proper loss, so scale brings them clos

Applications

When More Objectives Make Identification Easier: The Role of Preferences and Feedback

arXiv:2610.18577v1 Announce Type: new Abstract: Best-arm identification (BAI) aims to identify the optimal arm using as few samples as possible. While most existing work focuses on a single objective,

applicationsarxiv-cs-lg

When pose graphs are not enough: a failure analysis of pose-graph-based camera estimation for Gaussian splatting

arXiv:2610.14090v1 Announce Type: new Abstract: Exterior Gaussian-splat twins for real-estate listings are captured to a fixed recipe: a consumer drone orbits a single property in several overlapping

researcharxiv-cs-ro

WisdomNet: trustable machine learning toward error-free classification

arXiv:2610.15299v1 Announce Type: new Abstract: Misclassification is a critical problem in many machine learning applications. Since even the classifier models with high accuracy (e.g., > 95%) still i

A Categorical Atlas of Parameter-Efficient Fine-Tuning: Cones, Orbits, and Gauges

arXiv:2610.12728v1 Announce Type: new Abstract: Parameter-efficient fine-tuning has hundreds of methods, usually compared by parameter count and benchmark score. We compare them as maps. A pretrained

A Multi-Agent LLM System for Automated Tuning of Specialized Neural Network Models

arXiv:2610.13663v1 Announce Type: new Abstract: Many machine learning approaches are two-level: an expert selects a class of methods, within this class the choice of key components is critical and usu

agentsarxiv-cs-ai

Industry

🧬 🤗 A new atlas of microproteins in the human brain is now @huggingface! It brings together more than 1,000 microproteins, including one t…

🧬 🤗 A new atlas of microproteins in the human brain is now @huggingface! It brings together more than 1,000 microproteins, including one that’s reduced in Alzheimer’s and appears to play a role in how

industryclem-delangue--x

Activating the Annotator: Operationalising Lived Experience in Data Annotation

arXiv:2610.12701v1 Announce Type: new Abstract: Perspectivist NLP has established that annotator positionality shapes labels for subjective tasks, but the field still lacks methods for operationalisin

researcharxiv-cs-cl

Adaptive Variance Reduction without Knowing Oracle Smoothness

arXiv:2610.13799v1 Announce Type: cross Abstract: Variance reduction improves the convergence rate of stochastic non-convex optimization under the average smoothness condition. Existing adaptive varia

Asymptotic Analysis of Discrete-Time Self-Attention Dynamics in Transformers

arXiv:2610.13518v1 Announce Type: cross Abstract: In machine learning, a self-attention dynamics describes the attention mechanism of transformers, and is obtained treating the layer index of a transf