Search: “framework”

Fast Convergence through Distributed Augmentation for Class-Imbalanced Federated Learning

arXiv:2610.05279v1 Announce Type: new Abstract: In federated learning, mitigating class imbalance is essential to improve minority-class performance. A common approach to address this problem is to au

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Feature identification for parameter extraction and defect detection using machine learning

DGX agent

arXiv:2610.05812v1 Announce Type: new Abstract: Process control of advanced semiconductor nodes is not only pushing the limits of metrology equipment requirements in terms of resolution and throughput

model-releasesarxiv-cs-lg

6 Oct 2026

Hardware

FedUHD: Unsupervised Federated Learning using In-Memory Hyperdimensional Computing

arXiv:2508.12021v2 Announce Type: replace Abstract: Unsupervised federated learning (UFL) enables privacy-preserving distributed training without data labeling, yet practical deployment remains challe

hardwarearxiv-cs-lg

Fine-Grained Caching for Diffusion Transformers with Few Calibration Conditions

arXiv:2512.05134v2 Announce Type: replace Abstract: Diffusion transformers require repeated denoiser evaluations, making image and video generation computationally expensive. We propose a training-fre

safetyarxiv-cs-cv

Flow Policies as Actions of Skill-Level World Models: Learned and Symbolic Abstractions for Long-Horizon Planning

arXiv:2610.04767v1 Announce Type: cross Abstract: Latent world models enable robots to plan by predicting the consequences of actions. Planning long tasks with control-rate actions requires many predi

FOCUS: Fine-Grained Open-Vocabulary Change Detection for Uncertainty-Aware Semi-Static Scenes

model-releasesarxiv-cs-ro

arXiv:2610.05639v1 Announce Type: new Abstract: Autonomous robots operating over long periods must keep their environmental memory up to date as the world changes between visits. In semi-static enviro

ForeAct3D: Policy-Grounded Future World Modeling for VLA Policies

arXiv:2610.04607v1 Announce Type: cross Abstract: Robots need to anticipate how their actions will change the world, since manipulation success hinges on the resulting contacts and object motions. How

safetyarxiv-cs-cv

FORGE: Verification-Gated Behavioral Repair for Generative Language Models

arXiv:2610.05190v1 Announce Type: cross Abstract: Generative large language models (LLMs) inherit undesirable behaviors from pre-training, including demographic bias and toxic generation, that often e

local-aiarxiv-cs-lg

FoSeRL: Formal Sequential Robustness Certification for Reinforcement Learning Policies

arXiv:2610.04754v1 Announce Type: new Abstract: Even a few action perturbations can substantially degrade the performance of a deployed decision policy. Certifying the resulting return loss is challen

Fractal Cross Product: Theory, Differentiable Implementation and Application to Medical Image Analysis

arXiv:2610.03755v1 Announce Type: new Abstract: The magnitude of the generalized Euclidean cross product is a Gram volume whose degree under common scaling is fixed by the integer dimension of the spa

researcharxiv-cs-cv

Frame-Level Temporal Alignment for Human-to-Robot Visual Adaptation

arXiv:2610.04372v1 Announce Type: cross Abstract: Transferring visual representations pretrained on human videos to robot manipulation requires learning reliable correspondences between human and robo

FreeLoc: Online Floorplan Localization via Diffusion-Aided Pose Refinement

arXiv:2610.05011v1 Announce Type: cross Abstract: Floorplans provide compact and widely available geometric maps for indoor localization, but existing high-performing floorplan-based methods still con

local-aiarxiv-cs-cv

From Transformation to Target State: Rethinking Query Representation for Zero-Shot Composed Image Retrieval

model-releasesarxiv-cs-cv

arXiv:2610.05993v1 Announce Type: new Abstract: Composed image retrieval (CIR) aims to retrieve a desired target image from a query consisting of a reference image and a modification text. This task e

From Transformers to Weighted Automata: Towards the Verification of Large Language Models

arXiv:2610.04569v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in safety-critical settings, yet their black-box nature makes it difficult to provide formal gu

FTD-GNO: Memory-Efficient Graph Neural Operators through Functional Tensor Decomposition of the Kernel

arXiv:2610.04212v1 Announce Type: new Abstract: Graph Neural Operators (GNOs) provide flexible surrogate models for learning solution operators of partial differential equations (PDEs). However, stand

Future Anchored Verification and Online Recovery for World Action Models

arXiv:2610.06280v1 Announce Type: new Abstract: World action models (WAMs) have emerged as a promising paradigm for robotic manipulation. They act by first predicting how a task should be performed an

GAMBIT: Learning to Plan Continuous Multi-Robot Trajectories

arXiv:2610.06290v1 Announce Type: cross Abstract: GAMBIT is an opening chess move in which a player sacrifices a piece, typically a pawn, to gain a positional advantage later in the game. Analogously,

Gated Graph Neural Networks for Learning Hidden Independent Cascade Dynamics

arXiv:2610.04033v1 Announce Type: cross Abstract: Information and infectious diseases spread through social networks, but the spreading probabilities driving them are hard to estimate without per-node

Gated Target Propagation for Compositional Generalization in Continual Learning

arXiv:2610.04649v1 Announce Type: new Abstract: Continual learning is typically framed as acquiring new knowledge without catastrophically forgetting previous tasks. However, a flexible continual lear

local-aiarxiv-cs-lg

Generating the Wild: Individual-Consistent Image-to-Video Generation for Wildlife

arXiv:2610.05587v1 Announce Type: new Abstract: Individual-level wildlife identification often suffers from data scarcity, as varying observations of the same animal under diverse poses, viewpoints, a

local-aiarxiv-cs-cv

Tutorials

GeoFunFlow: Geometric function flow matching for joint probabilistic inference of physical fields and complex geometries

arXiv:2509.24117v2 Announce Type: replace Abstract: Inverse problems governed by partial differential equations (PDEs) arise widely in science and engineering, but are often ill-posed and limited by s

tutorialsarxiv-cs-lg

GFPack++: Attention-Driven Gradient Fields for Optimizing 2D Irregular Packing

arXiv:2406.07579v2 Announce Type: replace-cross Abstract: 2D irregular packing is a classic combinatorial optimization problem with various applications, such as material utilization and texture atlas

GNN-CB: A Graph Neural Network Competition Benchmark for Human and LLM Evaluation

arXiv:2610.05387v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong performance on coding and reasoning benchmarks; however, their ability to solve graph-structured m

Applications

GOTT: Object-centric Dexterous Manipulation with a Reusable Cross-Embodiment Primitive

arXiv:2610.03861v1 Announce Type: cross Abstract: Foundation models and large-scale human data provide rich sources of manipulation intent, but translating this intent into multi-fingered robot behavi

applicationsarxiv-cs-cv

GR-LIO: A Local Ground-Aware LiDAR-Inertial Odometry System Using Body-to-Ground Height

model-releasesarxiv-cs-ro

arXiv:2610.05546v1 Announce Type: new Abstract: LiDAR-inertial odometry (LIO) is widely used for state estimation in ground-based autonomous mobile robots. However, the geometric constraints provided

GRAM: Correcting Frozen Time-Series Foundation Models via Graph-Retrieved Amplitude Memory

arXiv:2610.04827v1 Announce Type: new Abstract: Time-series foundation models (TSFMs) enable zero-shot forecasting through large-scale cross-domain pretraining, while retrieval augmentation further im

Applications

Graph Data Augmentation via Contrastive Generator Inversion (exttt{DCBA})

arXiv:2610.05653v1 Announce Type: new Abstract: Graphs provide a natural representation of many complex systems, ranging from social platforms to ecosystems. However, the development of graph-based ma

applicationsarxiv-cs-lg

GUIDE: Reinforcement Learning for Behavioral Action Support in Type 1 Diabetes

arXiv:2604.00385v2 Announce Type: replace Abstract: Type 1 diabetes (T1D) management requires continuous adjustment of insulin and lifestyle behaviors to maintain blood glucose within a safe target ra

Harnessing Multimodal Large Language Models for Training-Free Human-Object Interaction Detection

model-releasesarxiv-cs-cv

arXiv:2610.06394v1 Announce Type: new Abstract: Human-object interaction (HOI) detection aims to localize human-object pairs and recognize their interactions. Traditional supervised methods perform st

Hierarchical Reinforcement Learning for Collision-Free Locomotion of an Underactuated Biped

arXiv:2610.05855v1 Announce Type: new Abstract: A bipedal robot cannot deviate from its path to avoid an obstacle without disturbing its balance, and this coupling is most severe on underactuated plat

HLA-WM: Hybrid Linear Attention for Long-Horizon Video World Models

arXiv:2610.05739v1 Announce Type: new Abstract: Long-horizon video world models require persistent memory to preserve scene consistency over extended rollouts. Softmax attention retains the full gener

researcharxiv-cs-cv

How RL Reshapes LLM Reasoning: Transferability, Coverage, and Scaling Laws

arXiv:2610.04158v1 Announce Type: new Abstract: Recent studies on reinforcement learning (RL) report seemingly conflicting evidence about large language model (LLM) reasoning. Training on mathematics

Human Behavior-Informed Crash Scenario Generation with Real-World Crash Priors for Autonomous Vehicle Safety Evaluation

arXiv:2610.04366v1 Announce Type: new Abstract: Reliable safety evaluation of autonomous vehicles (AVs) is essential to improving road safety, yet it depends critically on realistic simulation of rare

Humanoid Rickshaw Pulling: Whole-Body Locomotion under Coupled Wheeled Loads

arXiv:2610.04238v1 Announce Type: cross Abstract: Humanoid robots could transport payloads substantially heavier than themselves by pulling passive wheeled vehicles instead of carrying the load. This

Inferring physical fields in coupled systems with unknown parameters from incomplete observations using physics-constrained attentive neural operators

arXiv:2610.05723v1 Announce Type: new Abstract: Given incomplete measurements of a single physical field in a coupled system with unknown parameters, can we infer its full physical state and identify

Inspect Robots: Evaluating the Capabilities and Safety of Embodied AI

arXiv:2610.06306v1 Announce Type: new Abstract: General purpose language models are increasingly able to control robotic hardware. Understanding the capabilities and safety of these models when embodi

Integrating Survival-Based Aging Models with Data-Driven RUL Prognostics

arXiv:2610.06128v1 Announce Type: new Abstract: Predictive maintenance requires reliable remaining useful life (RUL) estimation. Existing methods mainly follow two paradigms: wear-based aging models t

Tutorials

Inverse Mixed-Integer Programming: Learning Constraints then Objective Functions

arXiv:2510.04455v3 Announce Type: replace-cross Abstract: Data-driven inverse optimization for mixed-integer linear programs (MILPs), which seeks to learn an objective function and constraints consist

tutorialsarxiv-cs-lg

Kinematics-Centric Continuous Sign Language Retrieval with Gloss-Guided Boundary-Aware Alignment

arXiv:2610.05306v1 Announce Type: new Abstract: Sign language-text alignment remains a fundamental challenge for text-driven sign language understanding. Existing methods predominantly rely on appeara

local-aiarxiv-cs-cv

KineWorld: Action-Induced Transport Fields for Embodied World Modeling

arXiv:2610.06349v1 Announce Type: new Abstract: Embodied world models predict the visual consequences of candidate actions before execution. However, existing action-conditioned world models often ado

researcharxiv-cs-cv

KVE-KD: Key Visual Evidence-Guided Knowledge Distillation for Vision-Language Models

arXiv:2610.03842v1 Announce Type: new Abstract: Knowledge distillation is crucial for deploying vision-language models on resource-constrained devices. However, existing methods typically impose unifo

Latent Similarity Gaussian Processes: A Theory-Grounded Approach to Personalized Suicide-Risk Forecasting for Clinical Decision-Support

arXiv:2610.06355v1 Announce Type: cross Abstract: Forecasting suicide risk is difficult due to the high heterogeneity of patients and the low base rate of suicide-related events (SREs). We present Lat

LD-EnFF: Latent-Dynamics Ensemble Flow Filtering for Data Assimilation with Sparse Observations

arXiv:2610.04034v1 Announce Type: new Abstract: Data assimilation combines model forecasts with noisy, incomplete observations to estimate the evolving state of a dynamical system. Existing methods fa

Applications

Learning Granger Causality under Latent Confounding via Intervention-Induced Heterogeneity

arXiv:2510.19138v2 Announce Type: replace Abstract: Granger causality characterizes directed predictive dependencies in multivariate time series, but recovering such dependencies becomes challenging i

applicationsarxiv-cs-lg

Learning Interpretable Differentiable Logic Networks for Tabular Regression

arXiv:2505.23615v2 Announce Type: replace Abstract: Neural networks (NNs) achieve outstanding performance in many domains; however, their decision processes are often opaque and their inference can be

Learning Latent Protein Languages for Autoregressive Generation

arXiv:2610.03978v1 Announce Type: new Abstract: Autoregressive transformers remain comparatively weak for protein sequence and structure generation. We study the role of target representation: amino a

Tutorials

Learning Subject-Specific Anatomical Representations via Manifold Expansion: Application to Accelerated Multi-Contrast MRI

arXiv:2610.04028v1 Announce Type: new Abstract: Clinical MRI routinely acquires multiple contrast-weighted images of the same anatomy for complementary tissue characterization. However, current accele

tutorialsarxiv-cs-cv

Learning to Read the Contextual Tokens in Diffusion Transformers

arXiv:2610.06844v1 Announce Type: new Abstract: Multimodal Diffusion Transformers (MM-DiTs) jointly process visual and textual representations throughout generation. These models repeatedly update the

safetyarxiv-cs-cv