Search: “models”

Closing the Motion Execution Gap: From Semantic Motion Task Constraints to Kinematic Control

arXiv:2605.12053v1 Announce Type: new Abstract: This paper addresses the Motion Execution Gap, the disconnect between high-level symbolic task descriptions using semantic constraints and executable ro

researcharxiv-cs-ro

Research

Contrastive Learning under Noisy Temporal Self-Supervision for Colonoscopy Videos

DGX agent

arXiv:2605.12320v1 Announce Type: new Abstract: Learning robust representations of polyp tracklets is key to enabling multiple AI-assisted colonoscopy applications, from polyp characterization to auto

researcharxiv-cs-cv

13 May 2026

counterpoint: i have no interest in a company that says we will roll everything out to anybody regardless of how dangerous it is.

counterpoint: i have no interest in a company that says we will roll everything out to anybody regardless of how dangerous it is. boris cherny recently says that, 'anthropic has no plans to roll out m

safetygary-marcus--x

Decomposing the Generalization Gap in PROTAC Activity Prediction: Variance Attribution and the Inter-Laboratory Ceiling

arXiv:2605.11764v1 Announce Type: new Abstract: Machine-learning predictors of biochemical activity often exhibit large random-split-to-leave-one-target-out generalisation gaps that have been document

Applications

Detecting In-Person Conversations in Noisy Real-World Environments with Smartwatch Audio and Motion Sensing

arXiv:2507.12002v2 Announce Type: replace Abstract: Social interactions play a crucial role in shaping human behavior, relationships, and societies. It encompasses various forms of communication, such

applicationsarxiv-cs-lg

Discrete Flow Matching for Offline-to-Online Reinforcement Learning

arXiv:2605.12379v1 Announce Type: new Abstract: Many reinforcement learning (RL) tasks have discrete action spaces, but most generative policy methods based on diffusion and flow matching are designed

Do multi-agent systems make LLM reasoning better? Most AI devs assume that it should. But this new paper shows that this is often not the ca…

Do multi-agent systems make LLM reasoning better? Most AI devs assume that it should. But this new paper shows that this is often not the case. It ran 22,500 deterministic trajectories across GAIA, SW

safetydair-ai--x

Dynamic Full-body Motion Agent with Object Interaction via Blending Pre-trained Modular Controllers

arXiv:2605.11369v1 Announce Type: new Abstract: Generating physically plausible dynamic motions of human-object interaction (HOI) remains challenging, mainly due to existing HOI datasets limited to st

agentsarxiv-cs-cv

ECTO: Exogenous-Conditioned Temporal Operator for Ultra-Short-Term Wind Power Forecasting

arXiv:2605.12196v1 Announce Type: new Abstract: Accurate ultra-short-term wind power forecasting is critical for grid dispatch and reserve management, yet remains challenging due to the non-stationary

Emergent Communication between Heterogeneous Visual Agents through Decentralized Learning

arXiv:2605.11695v1 Announce Type: new Abstract: Symbols are shared, but perception is private. We study emergent communication between heterogeneous visual agents through decentralized learning, askin

EndoVGGT: GNN-Enhanced Depth Estimation for Surgical 3D Reconstruction

arXiv:2603.24577v2 Announce Type: replace Abstract: Accurate 3D reconstruction of deformable soft tissues is essential for surgical robotic perception. However, low-texture surfaces, specular highligh

researcharxiv-cs-cv

Exploring Token-Space Manipulation in Latent Audio Tokenizers

arXiv:2605.11192v1 Announce Type: cross Abstract: Neural audio codecs provide compact discrete representations for speech generation and manipulation. However, most codecs organize tokens as frame-lev

Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attention

arXiv:2605.11752v1 Announce Type: new Abstract: Federated learning relies on effective client selection to alleviate the performance degradation caused by data heterogeneity. Most existing methods ass

FedOUI: OUI-Guided Client Weighting for Federated Aggregation

arXiv:2605.11571v1 Announce Type: new Abstract: Federated learning usually aggregates client updates using dataset size or gradient-level criteria, while overlooking internal signals about how each cl

Tutorials

Fine-tune LLM with Databricks Unity Catalog and Amazon SageMaker AI

In this post, we demonstrate how to build a secure, complete LLM fine-tuning workflow that integrates Unity Catalog with Amazon SageMaker AI using Amazon EMR Serverless for preprocessing. The solution

tutorialsaws-ml-blog

Forecast-aware Gaussian Splatting for Predictive 3D Representation in Language-Guided Pick-and-Place Manipulation

arXiv:2605.11144v1 Announce Type: new Abstract: We introduce Forecast-aware Gaussian Splatting (Forecast-GS), a predictive 3D representation framework for language-conditioned robotic manipulation. Wh

agentsarxiv-cs-ro

From Image Hashing to Scene Change Detection

arXiv:2605.12259v1 Announce Type: new Abstract: Image hashing provides compact representations for efficient storage and retrieval but is inherently limited to global comparison and cannot reason abou

local-aiarxiv-cs-cv

From Reaction to Anticipation: Proactive Failure Recovery through Agentic Task Graph for Robotic Manipulation

arXiv:2605.11951v1 Announce Type: new Abstract: Although robotic manipulation has made significant progress, reliable execution remains challenging because task failures are inevitable in dynamic and

agentsarxiv-cs-ro

Fused Gromov-Wasserstein Distance with Feature Selection

arXiv:2605.12161v1 Announce Type: new Abstract: Fused Gromov-Wasserstein (FGW) distances provide a principled framework for comparing objects by jointly aligning structure and node features. However,

GaitProtector: Impersonation-Driven Gait De-Identification via Training-Free Diffusion Latent Optimization

arXiv:2605.12431v1 Announce Type: new Abstract: Conventional gait de-identification methods often encounter an inherent trade-off: they either provide insufficient identity suppression or introduce sp

researcharxiv-cs-cv

GEAR: Granularity-Adaptive Advantage Reweighting for LLM Agents via Self-Distillation

arXiv:2605.11853v1 Announce Type: cross Abstract: Reinforcement learning has become a widely used post-training approach for LLM agents, where training commonly relies on outcome-level rewards that pr

safetyarxiv-cs-cl

Generative AI for Visualizing Highway Construction Hazards Through Synthetic Images and Temporal Sequences

arXiv:2605.11276v1 Announce Type: new Abstract: Highway construction workers face a high risk of serious injury or death. Image-based training materials depicting hazardous scenarios are essential for

GeoQuery: Geometry-Query Diffusion for Sparse-View Reconstruction

arXiv:2605.12399v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has emerged as a prominent paradigm for 3D reconstruction and novel view synthesis. However, it remains vulnerable to sever

researcharxiv-cs-cv

Industry

Grok 4.3 is better than people think.

Elon Musk posted on X claiming that Grok 4.3 performs better than public perception suggests, though the specific performance improvements or capabilities are not detailed in the title. This statement

industryelon-musk--x

H3D-MarNet: Wavelet-Guided Dual-Path Learning for Metal Artifact Suppression and CT Modality Transformation for Radiotherapy Workflows

arXiv:2605.12252v1 Announce Type: new Abstract: Metal artifacts in computed tomography (CT) severely degrade image quality, compromising diagnostic accuracy and radiotherapy planning, especially in ca

local-aiarxiv-cs-cv

Applications

Hide to See: Reasoning-prefix Masking for Visual-anchored Thinking in VLM Distillation

arXiv:2605.11651v1 Announce Type: cross Abstract: Recent think-answer approaches in VLMs, such as Qwen3-VL-Thinking, boost reasoning performance by leveraging intermediate thinking steps before the fi

applicationsarxiv-cs-cl

Hypernetworks for Dynamic Feature Selection

arXiv:2605.12278v1 Announce Type: new Abstract: Dynamic feature selection (DFS) is a machine learning framework in which features are acquired sequentially for individual samples under budget constrai

In-Context Multi-Objective Optimization

arXiv:2512.11114v2 Announce Type: replace Abstract: Balancing competing objectives is omnipresent across disciplines, from drug design to autonomous systems. Multi-objective Bayesian optimization is a

Interesting paper. What I like about this is that it is a relatively low-commitment attention modification. I.e., one can use it during most…

researchsebastian-raschka--x

Interesting paper. What I like about this is that it is a relatively low-commitment attention modification. I.e., one can use it during most of training, switch back to vanilla attention near the end,

Introducing Environmental Constraints to Grasping Strategies for Paper-Like Flexible Materials Using a Soft Gripper

arXiv:2605.11714v1 Announce Type: new Abstract: Robotic manipulation of flexible objects is widely required in both industrial and service applications. Among such objects, paper-like materials exhibi

researcharxiv-cs-ro

just tried this out and it one-shotted* this video: 'before the agent does anything' *i generated the narrative using chatgpt and used that …

just tried this out and it one-shotted* this video: 'before the agent does anything' *i generated the narrative using chatgpt and used that as a prompt. featuring: @e2b @runanywhereai @composio @mem0a

agentsyohei-nakajima--x

Kimi track - First place: JumpFoundry by @evvaaannnn A Hermes Agent plugin where the designer hand-draws one or two glyphs and Hermes Agent …

Kimi track - First place: JumpFoundry by @evvaaannnn A Hermes Agent plugin where the designer hand-draws one or two glyphs and Hermes Agent generates the rest, acting as coordinator that routes per-gl

agentskimi-moonshot--x

LA-Sign: Looped Transformers with Geometry-aware Alignment for Skeleton-based Sign Language Recognition

arXiv:2603.29057v2 Announce Type: replace Abstract: Skeleton-based isolated sign language recognition (ISLR) demands fine-grained understanding of articulated motion across multiple spatial scales, fr

Applications

Learning plug-in surrogate endpoints for randomized experiments

arXiv:2605.12051v1 Announce Type: new Abstract: Surrogate endpoints are used in place of long-term outcomes in randomized experiments when observing the real outcome for a large enough cohort is prohi

applicationsarxiv-cs-lg

Limits of Learning Linear Dynamics from Experiments

arXiv:2605.12010v1 Announce Type: new Abstract: Learning governing dynamics from data is a common goal across the sciences, yet it is only well-posed when the underlying mechanisms are identifiable. I

LucidFlux: Caption-Free Photo-Realistic Image Restoration via a Large-Scale Diffusion Transformer

arXiv:2509.22414v4 Announce Type: replace Abstract: Image restoration (IR) aims to recover images degraded by unknown mixtures while preserving semanticsconditions under which discriminative restorers

LychSim: A Controllable and Interactive Simulation Framework for Vision Research

arXiv:2605.12449v1 Announce Type: new Abstract: While self-supervised pretraining has reduced vision systems' reliance on synthetic data, simulation remains an indispensable tool for closed-loop optim

agentsarxiv-cs-cv

Minimax Rates and Spectral Distillation for Tree Ensembles

arXiv:2605.11841v1 Announce Type: cross Abstract: Tree ensembles such as random forests (RFs) and gradient boosting machines (GBMs) are among the most widely used supervised learners, yet their theore

Missingness-MDPs: Bridging the Theory of Missing Data and POMDPs

arXiv:2605.12262v1 Announce Type: cross Abstract: We introduce missingness-MDPs (miss-MDPs), a novel subclass of partially observable Markov decision processes (POMDPs) that incorporates the theory of

More Than Meets the Eye: A Semantics-Aware Traffic Augmentation Framework for Generalizable Website Fingerprinting

arXiv:2605.11402v1 Announce Type: new Abstract: Deep learning-based website fingerprinting has emerged as an effective technique for inferring the websites users visit. Although existing methods achie

Much of Geospatial Web Search Is Beyond Traditional GIS

arXiv:2605.11336v1 Announce Type: cross Abstract: Web search queries concern place far more often than existing labelling schemes suggest, yet the landscape of geospatial web search queries - what peo

researcharxiv-cs-cl

Muon is Not That Special: Random or Inverted Spectra Work Just as Well

arXiv:2605.11181v1 Announce Type: new Abstract: The recent empirical success of the Muon optimizer has renewed interest in non-Euclidean optimization, typically justified by similarities with second-o

local-aiarxiv-cs-lg

Neural-Schwarz Tiling for Geometry-Universal PDE Solving at Scale

arXiv:2605.12343v1 Announce Type: new Abstract: Most learned PDE solvers follow a global-surrogate paradigm: a neural operator is trained to map full problem descriptions to full solution fields for a

local-aiarxiv-cs-lg

Offline Policy Evaluation for Manipulation Policies via Discounted Liveness Formulation

arXiv:2605.11479v1 Announce Type: new Abstract: Policy evaluation is a fundamental component of the development and deployment pipeline for robotic policies. In modern manipulation systems, this probl

safetyarxiv-cs-ro

On the Importance of Multistability for Horizon Generalization in Reinforcement Learning

arXiv:2605.12206v1 Announce Type: new Abstract: In reinforcement learning (RL), agents acting in partially observable Markov decision processes (POMDPs) must rely on memory, typically encoded in a rec

Tutorials

On What We Can Learn from Low-Resolution Data

arXiv:2605.12168v1 Announce Type: new Abstract: Artificial intelligence systems typically rely on large, centrally collected datasets, a premise that does not hold in many real-world domains such as h

tutorialsarxiv-cs-lg

Operator Spectroscopy of Trained Lattice Samplers

arXiv:2605.11199v1 Announce Type: cross Abstract: Trained lattice samplers are usually judged by the ensembles they generate. Here we instead analyze the trained field-space function itself: a flow-ma

Partition Tree: Conditional Density Estimation over General Outcome Spaces

arXiv:2602.04042v2 Announce Type: replace Abstract: We propose Partition Tree, a novel tree-based framework for conditional density estimation over general outcome spaces that supports both continuous