Search: “papers”

MIND: Microstructure INverse Design with Generative Hybrid Neural Representation

arXiv:2502.02607v2 Announce Type: replace Abstract: The inverse design of microstructures plays a pivotal role in optimizing metamaterials with specific, targeted physical properties. While traditiona

safetyarxiv-cs-cv

Human

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Model Releases

Motif Channel Opened in a White-Box: Stereo Matching via Motif Correlation Graph

DGX agent

arXiv:2411.12426v3 Announce Type: replace Abstract: Real-world applications of stereo matching, such as autonomous driving, place stringent demands on both safety and accuracy. However, learning-based

model-releasesarxiv-cs-cv

6 Oct 2026

Applications

Multi-Marginal Schrodinger Bridge Matching

arXiv:2510.16587v2 Announce Type: replace-cross Abstract: Understanding the continuous evolution of populations from discrete temporal snapshots is a critical research challenge, particularly in field

applicationsarxiv-cs-lg

Multi-Task Partially Supervised Learning for Super-Resolution and Semantic Segmentation on Earth Observation data

arXiv:2610.06389v1 Announce Type: new Abstract: Super-resolution and semantic segmentation are known to benefit one another, especially in the Earth observation context. However, learning both tasks i

NM-LIO: Multiple LiDAR-Inertial Odometry Addressing LiDAR Measurement Noise Discrepancy

arXiv:2610.04551v1 Announce Type: new Abstract: Multiple LiDAR-inertial odometry methods have been widely applied in robotic applications owing to their enhanced accuracy and reliability. However, ado

researcharxiv-cs-ro

Occlusion-Aware Contingency Safety-Critical Planning for Autonomous Driving

arXiv:2502.06359v3 Announce Type: replace Abstract: Ensuring safe driving while maintaining travel efficiency for autonomous vehicles in dynamic and occluded environments is a critical challenge. This

OccStress: Stress-Testing the 4D Occupancy Forecasting Chain

arXiv:2512.15621v2 Announce Type: replace Abstract: Occupancy world models use historical occupancy states to forecast future 3D scenes, but their robustness under corrupted temporal inputs remains po

Off-Policy Merging Beats On-Policy Self-Distillation for Continual Learning

arXiv:2610.05872v1 Announce Type: new Abstract: A long-standing goal of AI is a model that can continually learn and improve itself. On post-trained models, supervised finetuning (SFT) on new data oft

On the Trade-off Between Information Loss and Generalization in Sparse Attention

arXiv:2610.04424v1 Announce Type: new Abstract: To mitigate the quadratic complexity bottleneck of the Transformer, sparse attention has emerged as a pivotal technology. Despite the extensive empirica

One-Cycle Fault Classification and Faulted-Line Identification on the PROTECT-90 Dataset: An Initial Application Benchmark

arXiv:2610.04155v1 Announce Type: cross Abstract: Open electromagnetic-transient datasets are beginning to make reproducible learning-based protection studies possible, but the practical use of these

Online Target-less Radar-LiDAR-Camera Extrinsic Calibration via Joint Optimization

arXiv:2610.04552v1 Announce Type: new Abstract: Fusing radar, LiDAR, and camera enables robust perception in diverse and adverse conditions, but the fusion performance critically depends on accurate e

researcharxiv-cs-ro

PB-STDG: A Prediction-Based Short-Term Decentralized Greedy Guidance Algorithm for a Drone Road System

arXiv:2610.05122v1 Announce Type: new Abstract: In recent years, Unmanned Aerial Vehicles (UAVs) or drones have been increasingly adopted in urban environments for applications such as parcel delivery

Planning for Change: Reinforcement Learning Combined with Bounded Extremum Seeking for Robotic Control under Distribution Shift

arXiv:2604.01142v2 Announce Type: replace-cross Abstract: Reinforcement learning has shown strong performance in robotic manipulation, but learned policies often degrade in performance when test condi

Priced Guidance: Can Language Models Generate Future Research Ideas?

arXiv:2610.04976v1 Announce Type: new Abstract: We evaluate language models' capability to generate novel research ideas through the lens of compression. We aim to lower-bound the potentially tiny pro

Quantifying the Stability of Multi-Step Reasoning via Error Amplification

arXiv:2610.06404v1 Announce Type: new Abstract: We consider the stability of multi-step reasoning processes, which have extensive applications in language models, including chain-of-thought and algori

Ramp Metering Control via Hybrid State Deep Reinforcement Learning in Partially Observable Connected Vehicle Environments

arXiv:2610.06266v1 Announce Type: new Abstract: Freeway on-ramp merges are major sources of congestion, causing significant economic and environmental costs. While Deep Reinforcement Learning (DRL) of

Referring Multi-Object Tracking in Moving-Camera Videos via Global Motion Compensation

arXiv:2610.04185v1 Announce Type: new Abstract: Referring multi-object tracking (RMOT) takes a video and a language expression as input and tracks all referred objects. Many tracking requirements invo

Reinforcement Learning-Based Optimization of Workload-Aware Power Delivery Networks

arXiv:2610.06148v1 Announce Type: cross Abstract: Power Delivery Networks (PDNs) are critical components of modern VLSI chips, providing stable voltage levels while satisfying electromigration (EM) an

Local Ai

RepTC: Representation-Aware Optimization for Efficient Traffic Classification on Edge IoT Devices

arXiv:2610.04784v1 Announce Type: cross Abstract: Traffic classification (TC) is crucial to secure Internet of Things (IoT) networks, whose edge nodes often operate under privacy, bandwidth, and energ

local-aiarxiv-cs-lg

Resisting Adversarial Attacks in Deep Neural Networks using Diverse Decision Boundaries

arXiv:2208.08697v2 Announce Type: replace-cross Abstract: The security of deep learning (DL) systems is an extremely important field of study as they are being deployed in several applications due to

Safe Ergodic Control for Multi-Robot Systems via Quadratic Programming

arXiv:2610.05258v1 Announce Type: new Abstract: Ergodic control drives robots to spend time in each region in proportion to a spatial distribution of interest, making it well suited for dense spatiote

ScopeSAE: Model-Scope Feature Discovery with Interpretable Layer Selection

arXiv:2610.04905v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) are a central tool in mechanistic interpretability. However, existing SAEs are primarily trained per layer. The modeling subs

Sharp Deviations Bounds for Dirichlet Weighted Sums with Application to analysis of Bayesian algorithms

arXiv:2304.03056v1 Announce Type: cross Abstract: In this work, we derive sharp non-asymptotic deviation bounds for weighted sums of Dirichlet random variables. These bounds are based on a novel integ

Sparse-GS2Mesh: 3D Gaussian Splatting Guided by Novel Stereo Views and 2DGS for Sparse View Surface Reconstruction}

arXiv:2610.04203v1 Announce Type: new Abstract: Surface reconstruction under sparse-view settings remains challenging due to limited geometric cues. Volume rendering methods based on signed distance f

Stability and Generalization of Stochastic Nonconvex Optimization under Heavy-Tailed Noise: From Gradient Norm to Goldstein Stationarity

arXiv:2601.19730v2 Announce Type: replace Abstract: The empirical evidence indicates that stochastic optimization with heavy-tailed gradient noise is more appropriate to characterize the training of m

Tackling Sim-to-Real Mismatch Through Sampling-Based Disturbance Observers: From Analytical Models to Learned World Models

arXiv:2610.04896v1 Announce Type: new Abstract: Robotic controllers increasingly rely on analytical models, simulators, cost-query interfaces, and learned world models. However, physical deployment ca

researcharxiv-cs-ro

TAPDreamer: Transferable Adversarial Patches for World Action Models

arXiv:2610.06814v1 Announce Type: new Abstract: World models learn to predict how their environment will evolve, making them an important foundation for general-purpose robotic control. Yet world acti

Agents

The Optimization Landscape of Learning Compacted Context Models

arXiv:2610.05885v1 Announce Type: new Abstract: Many works approach continual learning through the lens of infinite context windows. As an agent puts more observation into context (concretely the KV c

agentsarxiv-cs-lg

The sublevel Flood bifiltration: towards scalable 2-parameter persistent homology

arXiv:2610.05441v1 Announce Type: cross Abstract: Multiparameter persistent homology is a rapidly developing branch of topological data analysis that improves the robustness of single-parameter persis

ThyCLIPNet: A BiomedCLIP-Guided Lightweight Attention-Enhanced DeepLabV3+ Framework for Robust Thyroid Nodule Segmentation

arXiv:2610.04743v1 Announce Type: new Abstract: Accurate thyroid ultrasound segmentation is often challenged by low contrast, speckle noise, and unclear boundaries. Although recent methods have improv

TIRMamba: A Thermal-Prior-Modulated State-Space Network for Sub-Million-Parameter Infrared Image Super-Resolution

arXiv:2610.05182v1 Announce Type: cross Abstract: Infrared image super-resolution is currently led by Mamba-based networks with 26 to 37 million parameters, which are difficult to deploy on the airbor

Towards causal effect estimation with learned instrument representations

arXiv:2602.10370v3 Announce Type: replace-cross Abstract: Instrumental variable (IV) methods mitigate bias from unobserved confounding in observational causal inference but rely on the availability of

Training-Free Transformer Merging via Sequential Local Operator Alignment

arXiv:2610.06415v1 Announce Type: new Abstract: Training-free model merging aims to combine multiple fine-tuned models into a single model without further optimization on labeled data. Yet, in transfo

Transform Trained Transformer for Accelerating Native 4K Video Generation

arXiv:2512.13492v2 Announce Type: replace Abstract: Native 4K (2176imes3840) video generation remains a critical challenge due to the quadratic computational explosion of full-attention as spatiotempo

Trust the View That Sees the Target: Mining Cross-View Conflicts for Reliability-Gated Disaster Damage Assessment

arXiv:2610.04327v1 Announce Type: new Abstract: After a disaster, building damage is assessed from overhead tiles and ground-level photographs, and most methods fuse the two views symmetrically, trust

Local Ai

TrustmeWatcher: An Application for Workplace Micro-Sensing and Explainable Well-Being Feedback

arXiv:2610.06657v1 Announce Type: new Abstract: Workplace sensing studies combine long-running behaviour traces with self-reports, yet the tools that collect those data often sit apart from the interf

local-aiarxiv-cs-lg

TurboPairFormer: Fast and Stable Protein Folding Model Training with an Optimized Triangle Attention Kernel

arXiv:2610.05854v1 Announce Type: new Abstract: Triangular attention is a core computation in AlphaFold3-style biomolecular models, with cubic cost in token count. Its shared pair bias adds a gradient

Two-Point Local Optimality in k-Means via Boundary-Point Screening

arXiv:2610.06182v1 Announce Type: new Abstract: Lloyd's algorithm and the discrete local (D-local) optimization method (Li et al., 2025) for k-means provide only weak local-optimality guarantees, and

UltraDub: Towards Authentic Dubbing by Unifying Visually-Steered Flow Learning and Trajectory Guidance

arXiv:2610.05932v1 Announce Type: new Abstract: Visual voice cloning requires intelligible, speaker-consistent speech synchronized with visible articulation. However, sequential multimodal conditionin

Tutorials

Ultrasound Operator Guidance Using World Modeling and Retrieval Based Action Planning

arXiv:2610.06008v1 Announce Type: cross Abstract: Ultrasound is widely used, but acquisition quality is heavily dependent on the operator's knowledge and expertise. With demand for examinations outpac

tutorialsarxiv-cs-cv

Variance-Aware Fine-Grained Gap-Dependent Bounds for Online Reinforcement Learning

arXiv:2610.04752v1 Announce Type: cross Abstract: We study model-free online reinforcement learning (RL) for episodic tabular Markov decision processes, focusing on both gap-dependent regret and polic

Tutorials

Variance-Optimal Control Variates for Learning with Black-box Feedback

arXiv:2610.04766v1 Announce Type: new Abstract: Modern models increasingly learn through black-box oracles such as humans, optimization solvers, and external tools that provide feedback without exposi

tutorialsarxiv-cs-lg

Applications

Virtual model control for compliant reaching under uncertainties

arXiv:2610.05695v1 Announce Type: new Abstract: Virtual Model Control (VMC) is an approach to design a controller for force-controlled robots in complex uncertain environments. While this method was p

applicationsarxiv-cs-ro

Hardware

Vision Transformer Ensembles for Panoramic Street Segmentation

arXiv:2610.06063v1 Announce Type: new Abstract: Semantic segmentation of street panoramas can support detailed descriptions of urban environments, yet small datasets and unequal training costs make mo

hardwarearxiv-cs-cv

Visual Swarm Navigation via Deep Reinforcement Learning and Evolutionary Hybrid Design

arXiv:2610.06400v1 Announce Type: new Abstract: Swarm robotics presents a robust and cost-effective paradigm for advanced automation in complex, dynamic environments, such as those encountered in sear

WASP: Weakly Aligned Spatiotemporal Pairs for Fetal Brain MRI-Ultrasound Learning

arXiv:2610.04601v1 Announce Type: new Abstract: Magnetic Resonance Imaging (MRI) is widely regarded as the optimal sensor for fetal brain analysis due to its superior soft-tissue contrast and anatomic

safetyarxiv-cs-cv

Your Unlearning Gives You Away: Identifying Erased Concepts in Diffusion Models

arXiv:2610.05601v1 Announce Type: new Abstract: Existing attacks on unlearned diffusion models assume that the erased concepts are known in advance and focus on recovering them. In practice, however,

A Polynomial-Time Algorithm for Variational Inequalities under the Minty Condition

arXiv:2504.03432v4 Announce Type: replace-cross Abstract: Solving (Stampacchia) variational inequalities (SVIs) is a foundational problem at the heart of optimization. However, this expressivity comes