Search: “research”

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation

arXiv:2602.08557v2 Announce Type: replace Abstract: Training non-prehensile manipulation policies in contact-rich settings is a core challenge in robotics. While Reinforcement Learning (RL) has demons

researcharxiv-cs-ro

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CoMet: Context and Multiplicity Decomposition for Multimodal Uncertainty Estimation

DGX agent

arXiv:2606.32012v1 Announce Type: cross Abstract: Uncertainty estimation has been a long-standing challenge in AI models; it amounts to 'knowing what you don't know,' and metacognition is notoriously

researcharxiv-cs-cv

1 Jul 2026

Communication-Aware Robot Execution for Cloud Inference under Spatially Heterogeneous Connectivity

arXiv:2606.31497v1 Announce Type: new Abstract: Cloud-hosted foundation models enable robots to use semantic reasoning beyond onboard computational limits. In this setting, the robot executes a curren

researcharxiv-cs-ro

Conditional Tropical Cyclogenesis Rates via Rare-Event Sampling in a Neural Weather Emulator

arXiv:2606.30920v1 Announce Type: cross Abstract: We couple Forward Flux Sampling (FFS), a non-equilibrium rare-event technique from statistical mechanics, to a neural weather emulator (SDL-WXFormer,

Conformalized Regression for Continuous Bounded Outcomes

arXiv:2507.14023v2 Announce Type: replace-cross Abstract: Regression problems with bounded continuous outcomes frequently arise in statistical and machine learning applications, such as the analysis o

Constrained Online Convex Optimization without Slater's Condition

arXiv:2606.31480v1 Announce Type: new Abstract: We study constrained online convex optimization with adversarial losses and stochastic or adversarial constraints. For stochastic constraints, existing

Creating Intelligence: A Computational Foundation for AGI

arXiv:2606.31819v1 Announce Type: new Abstract: This work introduces a new computational theory of mind grounded in set theory and hyperdimensional computing. Whereas traditional neural networks rely

Criticality-Constrained Iterative Pruning for Energy-Efficient Spiking Neural Networks via Combined Importance Scoring

arXiv:2606.30676v1 Announce Type: cross Abstract: Deploying spiking neural networks (SNNs) on neuromorphic hardware demands aggressive synaptic pruning while preserving temporal computation integrity.

Cross-Modal Hierarchical Fusion for from Multi-Sensor Ground Observation

arXiv:2606.30647v1 Announce Type: cross Abstract: Dense volumetric reconstruction of cloud microphysical fields from sparse ground-based instruments remains an open problem, largely because the availa

Cross-Resolution Distribution Matching for Diffusion Distillation

arXiv:2603.06136v2 Announce Type: replace Abstract: Diffusion distillation is central to accelerating image and video generation, yet existing methods are fundamentally limited by the denoising proces

CSO-LLM: Class Subspace Orthogonalization for Post-Training Backdoor Detection and Trigger Inversion in LLMs

arXiv:2606.31309v1 Announce Type: cross Abstract: While post-training backdoor detection and trigger inversion schemes have been developed for AIs used e.g. for images, there is a paucity of such meth

CVE-TTP KG: Knowledge Graph Linking Software Vulnerabilities to Attack Behaviors

arXiv:2606.31557v1 Announce Type: cross Abstract: In the evolving threat landscape, adversaries exploit software vulnerabilities to launch sophisticated attacks, challenging traditional defenses. Alth

Delta-JEPA: Learning Action-Sensitive World Models via Latent Difference Decoding

arXiv:2606.31232v1 Announce Type: new Abstract: Learning visual world models for planning requires compact latent dynamics that remain sensitive to actions, yet reconstruction-free joint-embedding obj

DEMUN: Fast and accurate discovery of music notation in very large collections

arXiv:2606.31956v1 Announce Type: cross Abstract: Much of written musical heritage is preserved and digitised at memory institutions: libraries, museums, and archives. Owing to their collection struct

DetPO: In-Context Learning with Multi-Modal LLMs for Few-Shot Object Detection

arXiv:2603.23455v2 Announce Type: replace Abstract: Multi-Modal LLMs (MLLMs) demonstrate strong visual grounding capabilities on popular object detection benchmarks like OdinW-13 and RefCOCO. However,

Diffusion-Based Material Regularization for Physics-Based Inverse Rendering

arXiv:2606.31065v1 Announce Type: new Abstract: Reconstructing physics-based 3D assets -- geometry, materials, and illumination -- from multi-view images is a core problem in computer graphics and vis

Diffusion Crossover: Defining Evolutionary Recombination in Diffusion Models via Noise Sequence Interpolation

arXiv:2604.14790v2 Announce Type: replace Abstract: Interactive Evolutionary Computation (IEC) provides a powerful framework for optimizing subjective criteria such as human preferences and aesthetics

Diffusion-warm sampling of the XY model enables fast thermalization at scale

arXiv:2606.30773v1 Announce Type: cross Abstract: We introduce a novel technique for scalable sampling of spin-system states with continuous symmetries using diffusion models. By applying our approach

Direction-Magnitude Decomposition for Low-Rank Matrix Optimization: Faster Convergence and Saddle-to-saddle Dynamics

arXiv:2606.31390v1 Announce Type: cross Abstract: Low-rank matrix optimization is often carried out via the Burer-Monteiro (BM) formulation, but choosing the factorization rank r is delicate and can s

Distilling the Essence: Efficient Reasoning Distillation via Sequence Truncation

arXiv:2512.21002v3 Announce Type: replace-cross Abstract: Distilling the capabilities from a large reasoning model (LRM) to a smaller student model often involves training on substantial amounts of re

Distortion-Corrected Diffusion MRI Using Rotated-View EPI and Joint Field-Map/Image Estimation with Gaussian Primitives

arXiv:2606.31521v1 Announce Type: cross Abstract: Echo Planar Imaging (EPI) is the standard acquisition technique for diffusion and functional neuroimaging, enabling rapid imaging but suffering from g

Do Not Break the Vessels: Structure-Preserving Mean Flow for Vascular Image Translation

arXiv:2606.31095v1 Announce Type: new Abstract: Reconstructing anatomically faithful vascular structures from clinically accessible imaging modalities is of substantial clinical significance. However,

DOGE showed they were morons with a series of gotcha moments: - DOGE claimed to have discovered USAID was paying for condoms for Gaza which …

DOGE showed they were morons with a series of gotcha moments: - DOGE claimed to have discovered USAID was paying for condoms for Gaza which they joked was for 'gay Hamas.' It turned out the condoms we

researchyann-lecun--x

Domain Adaptive Object Detection via Dual-Stream Bilevel-Cycle Optimization

arXiv:2606.31373v1 Announce Type: new Abstract: Cycle self-training (CST) breaks the shared classifier assumption of the standard self-training framework, which is effective for unsupervised domain ad

Donald Trump made more than $2 billion last year while lying that affordability issues are a hoax and prices are going down. Never in Americ…

Donald Trump made more than $2 billion last year while lying that affordability issues are a hoax and prices are going down. Never in American history has a man exploited the White House while in offi

researchyann-lecun--x

DPPE: Rethinking Camera-Based Positional Encoding for Scaling Multi-View Transformers

arXiv:2606.31585v1 Announce Type: cross Abstract: The remarkable scalability of Transformers has expanded their application to 3D computer vision, where camera-aware positional encoding is crucial for

Drop-In Perceptual Optimization for 3D Gaussian Splatting

arXiv:2603.23297v2 Announce Type: replace Abstract: Despite their output being ultimately consumed by human viewers, 3D Gaussian Splatting (3DGS) methods often rely on ad-hoc combinations of pixel-lev

Dynamic Gaussian Processes and the Vanilla-SPDE Exchange

arXiv:2606.31063v1 Announce Type: cross Abstract: Gaussian process inference is often limited by cubic computational costs, a challenge that becomes more pronounced in spatio-temporal settings where p

Dynamic Prediction of Alternating Recurrent Events via Neural Network

arXiv:2606.30889v1 Announce Type: cross Abstract: Alternating recurrent events -- event-times of a specific nature that trigger a secondary refractory period -- occur in a wide-range of fields, includ

Efficient Sampling with Discrete Diffusion Models: Sharp and Adaptive Guarantees

arXiv:2602.15008v2 Announce Type: replace Abstract: Diffusion models over discrete spaces have recently shown striking empirical success, yet their theoretical foundations remain incomplete. In this p

Elon Musk gets mad when people point out that DOGE and its USAID cuts killed thousands, if not hundreds of thousands, of babies, children, m…

I can't verify the specific claims in this post title. The source appears to be a social media post attributed to Yann LeCun discussing Elon Musk's response to criticism regarding DOGE (Department of

researchyann-lecun--x

EpiMask: Leveraging Epipolar Distance Based Masks in Cross-Attention for Satellite Image Matching

arXiv:2603.21463v2 Announce Type: replace Abstract: The deep-learning based image matching networks can now handle significantly larger variations in viewpoints and illuminations while providing match

Estimating Supply Incrementality in Two-sided Marketplaces: A Causal Machine Learning Approach

arXiv:2606.30999v1 Announce Type: new Abstract: In two-sided marketplaces with heterogeneous products, it is important to understand the causal relationship between additional supply and marketplace o

Estimating Velocity of Spheres from Rolling-Shutter Image(s)

arXiv:2606.31760v1 Announce Type: new Abstract: Rolling-shutter cameras introduce characteristic distortions when imaging fast moving objects, and these effects are typically treated as artifacts to b

Expected Gain-based Escalation in Vertical Federated Learning

arXiv:2606.31331v1 Announce Type: new Abstract: Collaborative inference can improve predictive performance by integrating complementary information across agents, but applying collaborative fusion to

Explaining Machine Learning and Memorization with Statistical Mechanics

arXiv:2606.31110v1 Announce Type: new Abstract: Artificial neural networks (NNs) and machine learning (ML) algorithms are poorly understood from a theoretical perspective, which makes it difficult to

FaceMoE: Mixture of Experts for Low-Resolution Face Recognition

arXiv:2606.32040v1 Announce Type: new Abstract: Low-resolution face recognition (LR-FR) remains a challenging task due to poor feature extraction and aggregation, as probe images often contain limited

FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning

arXiv:2606.31742v1 Announce Type: cross Abstract: Explainable AI (XAI) methods have demonstrated significant success in recent years at identifying relevant features in input data that drive deep lear

Few to Big: Prototype Expansion Network via Diffusion Learner for Point Cloud Few-shot Semantic Segmentation

arXiv:2509.12878v2 Announce Type: replace Abstract: Few-shot 3D point cloud semantic segmentation aims to segment novel categories using a minimal number of annotated support samples. However, prototy

FlexViT: A Flexible FPGA-based Accelerator for Edge Vision Transformers

arXiv:2606.31938v1 Announce Type: cross Abstract: Deploying Vision Transformer (ViT) models on edge platforms remains challenging due to their high computational demands and the architectural heteroge

From Idea to Prototype in an Afternoon: Scaffolded, AI-Assisted Rapid VA Prototyping

arXiv:2606.31311v1 Announce Type: cross Abstract: Testing a new visual-analytics idea usually takes months: one needs to find a realistic data set, clean it, and implement an interactive prototype. We

From Search to Synthesis: Training LLMs as Zero-Shot Workflow Generators

arXiv:2606.30704v1 Announce Type: cross Abstract: Large language models (LLMs) excel across a wide range of tasks, yet their instance-specific solutions often lack the structural consistency needed fo

FROST: Training-Free Few-Shot Segmentation with Frozen Features and Nonparametric Statistics

arXiv:2606.31136v1 Announce Type: new Abstract: Few-shot segmentation asks a model to delineate a target class in a query image from only a handful of annotated examples, a setting most acute in remot

Gated Multi-Graph Fusion via Graph Attention Networks for Alzheimer's Disease Detection

arXiv:2606.31186v1 Announce Type: cross Abstract: Spontaneous speech is a vital non-invasive biomarker for Alzheimer's Disease (AD), yet many systems overlook non-linear structural disruptions and cli

Gaussian Belief Propagation Network for Depth Completion

arXiv:2601.21291v2 Announce Type: replace Abstract: Depth completion aims to predict a dense depth map from a color image with sparse depth measurements. Although deep learning methods have achieved s

GaussLite: Online Task-Conditioned 3D Gaussian Splatting for Real-Time Robotic Mapping

arXiv:2606.30809v1 Announce Type: new Abstract: Existing 3D Gaussian Splatting (3DGS) systems distribute representation capacity uniformly across a scene, ignoring the fact that many downstream roboti

GeoNVS: Geometry Grounded Video Diffusion for Novel View Synthesis

arXiv:2603.14965v2 Announce Type: replace Abstract: Novel view synthesis requires strong 3D geometric consistency and the ability to generate visually coherent images across diverse viewpoints. While

Graph Coloring for Multi-Task Learning

arXiv:2509.16959v5 Announce Type: replace-cross Abstract: When different objectives conflict with each other in multi-task learning, gradients begin to interfere and slow convergence, thereby potentia