Search: “framework”

T-MOR: Learning Motion-Aware Skeleton Representations for Human Action Recognition

arXiv:2606.21607v1 Announce Type: new Abstract: Vision-language models such as CLIP have recently achieved strong performance on a wide range of visual understanding tasks. However, most existing mode

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TaigiSpeech: A Low-Resource Real-World Speech Intent Dataset and Preliminary Results with Scalable Data Mining In-the-Wild

DGX agent

arXiv:2603.21478v2 Announce Type: replace-cross Abstract: Speech technologies have advanced rapidly and serve diverse populations worldwide. However, many languages remain underrepresented due to limi

model-releasesarxiv-cs-lg

23 Jun 2026

Task-Agnostic Federated Continual Learning via Replay-Free Gradient Projection

arXiv:2509.21606v3 Announce Type: replace Abstract: Federated continual learning (FCL) enables collaborative model training across distributed clients on sequentially arriving tasks without revisiting

Temporal Graph Pattern Machine

arXiv:2601.22454v3 Announce Type: replace Abstract: Temporal graph learning is pivotal for deciphering dynamic systems, where the core challenge lies in explicitly modeling the underlying evolving pat

Temporal Self-Imitation Learning

arXiv:2606.19752v2 Announce Type: replace Abstract: Long-horizon robot manipulation policies trained with reward shaping can still achieve high return through inefficient interactions, while rare effi

Temporally Aware Densification for Dynamic 3D Gaussian Splatting

arXiv:2606.23212v1 Announce Type: new Abstract: Despite modeling temporal motion, dynamic 3D Gaussian Splatting (3DGS) methods still inherit a static densification strategy that is ill-suited for dyna

TEXEDO : Test Time Scaling for Controller-aware Language-conditioned Humanoid Motion Generation

arXiv:2606.22998v1 Announce Type: new Abstract: Text-conditioned motion generation is a promising interface for programming humanoid robots, yet current generators are often trained on human motion da

TF-SNO: Time-Frequency Gated Spectral Neural Operators for Learning Non-Stationary Partial Differential Equations

arXiv:2606.21189v1 Announce Type: new Abstract: Non-stationary partial differential equations (PDEs) arise throughout scientific computing, where the dominant frequency content and energy distribution

The Cost Geometry of Belief: finite-resource inference under noisy observation

arXiv:2606.21585v1 Announce Type: new Abstract: We equip the space of beliefs with a cost geometry (what it costs to pass from one belief to another): optimal transport in Wasserstein space, reweighte

The Energy Consumption of Transformer Fine-Tuning: A Roofline-Inspired Scaling Model

arXiv:2606.23546v1 Announce Type: new Abstract: Transformer-based models underpin modern natural language processing but incur rapidly growing computational and energy costs. As training scales in bot

The Geometry of Refusal: Linear Instability in Safety-Aligned LLMs

arXiv:2606.22686v1 Announce Type: cross Abstract: Modern Large Language Models (LLMs) rely on extensive safety alignment, yet the mechanistic basis of refusal remains opaque. In this work, we investig

The Keras team is gonna be at the AI Systems DevLabs in Sunnvale tomorrow. Come say hi if you're around!

researchfrancois-chollet--x

François Chollet announced that members of the Keras team would be attending the AI Systems DevLabs event in Sunnyvale and invited people to meet them there. This post indicates a scheduled appearance

The Power of Light: Improving Synthetic-to-Real Domain Adaptation through Physically-Based Indirect Illumination

arXiv:2606.22574v1 Announce Type: new Abstract: While synthetic data generation resolves the manual labeling bottleneck in computer vision, minimizing the syn-to-real domain gap requires optimizing re

The Trilemma of Truth in Large Language Models

arXiv:2506.23921v5 Announce Type: replace-cross Abstract: The public often attributes human-like qualities to large language models (LLMs), assuming that they 'know' certain things. In reality, LLMs e

Time Series Classification through Diffeomorphic Time Warping (DiffTW)

arXiv:2606.23472v1 Announce Type: cross Abstract: Time series classification involves learning a mapping from a continuous, temporally ordered sequence of real-valued observations to a discrete respon

safetyarxiv-cs-lg

Token Factory: Efficiently Integrating Diverse Signals into Large Recommendation Models

arXiv:2606.19635v2 Announce Type: replace-cross Abstract: Large Recommendation Models (LRMs) have demonstrated promising capabilities in industry-scale recommendation tasks. However, holistically inte

applicationsarxiv-cs-lg

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger

arXiv:2606.23362v1 Announce Type: cross Abstract: Diffusion models (DMs), despite their impressive capabilities across a wide range of generative tasks, have been shown to be vulnerable to backdoor at

Topological summaries of fingerprint ridge patterns carry identity information

arXiv:2606.22029v1 Announce Type: new Abstract: Fingerprints are the most widely deployed biometric. Verifying whether two impressions come from the same finger typically relies on minutiae, small lan

TopoRetarget: Interaction-Preserving Retargeting for Dexterous Manipulation

arXiv:2606.16272v2 Announce Type: replace Abstract: Human hand-object demonstrations provide dense reference motions for training dexterous manipulation reinforcement learning (RL) policies through re

Toward Machine Risk Perception: Integrating Trust Calibration and Precursor-Based Risk Estimation for Humanoid

arXiv:2606.20748v1 Announce Type: new Abstract: Humanoid robots are emerging as co-workers in smart manufacturing, yet their dynamic, human-like movements introduce safety risks that differ fundamenta

Toward Non-Expert Customized Congestion Control: Large Language Model-Assisted CCA Code Generation with eBPF Deployment

arXiv:2601.22461v2 Announce Type: replace-cross Abstract: General-purpose congestion control algorithms (CCAs) are designed to achieve general congestion control goals, but they may not meet the speci

applicationsarxiv-cs-lg

Towards Accurate and Robust Surveillance Roadside IVD via Trackletized Audio-Visual Reasoning

arXiv:2606.22299v1 Announce Type: new Abstract: Idling Vehicle Detection (IVD) seeks to determine, at the final frame of a video clip, whether any vehicle is idling, meaning the vehicle is stationary

Towards Practical Lossless Neural Compression for LiDAR Point Clouds

arXiv:2603.25260v2 Announce Type: replace Abstract: LiDAR point clouds are fundamental to various applications, yet the extreme sparsity of high-precision geometric details hinders efficient context m

Towards Robust Personalized Federated Learning: Vulnerability Assessment and Defense Co-Design

arXiv:2606.22782v1 Announce Type: new Abstract: The proliferation of IoT devices has fueled distributed edge systems to collect vast amounts of sensitive data, creating fertile ground for on-device ma

Towards Robust Training in NNGPT AutoML Pipeline: A Loss-Optimizer Pairing Selection Study

arXiv:2606.20933v1 Announce Type: new Abstract: The choice of loss function and optimizer is an important decision, that shapes further model training. Yet automated architecture search pipelines (Aut

Local Ai

Towards Whole Hand and Wrist Kinematic Tracking with a Wearable A-Mode Ultrasound Probe

arXiv:2606.22333v1 Announce Type: cross Abstract: A-mode ultrasound (US) has emerged as a promising modality for hand and wrist motion tracking. Prior works have mainly addressed static gesture classi

local-aiarxiv-cs-lg

Training Diffusion Policies via Prior-Mapping Co-Evolution

arXiv:2512.02581v3 Announce Type: replace Abstract: Reinforcement learning (RL) faces a persistent tension: policies that are stable to optimize (e.g., Gaussians) are often too simple to represent the

safetyarxiv-cs-lg

Training-Free Semantic Correction for Autoregressive Visual Models

arXiv:2606.22550v1 Announce Type: new Abstract: Autoregressive visual models (AVMs) based on next-scale prediction have emerged as a prominent paradigm for image and video synthesis. However, decompos

safetyarxiv-cs-cv

Local Ai

Trajectory Forcing: Structure-First Generation with Controllable Semantic Trajectories

arXiv:2606.22527v1 Announce Type: new Abstract: Diffusion and flow-based generative models produce strong images, yet their controllability remains largely endpoint-centric: users specify conditions a

local-aiarxiv-cs-cv

Local Ai

TriLift: Interpolation-Free Tri-Plane Lifting for Efficient 3D Perception on Embedded Systems

arXiv:2509.14641v2 Announce Type: replace Abstract: Dense 3D convolutions provide high accuracy for perception but are too computationally expensive for real-time robotic systems. Existing tri-plane m

local-aiarxiv-cs-ro

TriMotion: Modality-Agnostic Camera Control for Video Generation

arXiv:2606.20774v1 Announce Type: new Abstract: Camera motion control is essential for directing viewpoint changes in generative systems. However, existing methods typically condition the generation p

TSD: A Physics-Inspired Trajectory Saliency Detector for Efficient Imitation Learning

arXiv:2606.23371v1 Announce Type: new Abstract: For imitation learning in robotic manipulation, high data collection costs result in the scarcity of high quality data. In this paper, we leverage the i

applicationsarxiv-cs-ro

Turning Tabular Foundation Models into Graph Foundation Models

arXiv:2508.20906v3 Announce Type: replace Abstract: While foundation models have revolutionized fields such as natural language processing and computer vision, their potential in graph machine learnin

Agents

UECP: Uncertainty-Enhanced Collaborative Perception

arXiv:2606.23046v1 Announce Type: new Abstract: Collaborative perception serves as a pivotal solution to enhance the perception capability of individual agents in autonomous driving, where a core chal

agentsarxiv-cs-cv

Ultra-Peripheral Collisions as a Nuclear-Structure Interferometer with Interpretable Multitask Deep Learning

arXiv:2606.23353v1 Announce Type: cross Abstract: Precise knowledge of nuclear structure is essential across fundamental physics, yet probing these structures is notoriously difficult. To address this

Understanding Parallel Samplers in Masked Diffusion via Random Walks on Graphs

arXiv:2606.22976v1 Announce Type: new Abstract: In this paper, we propose using random walks on graphs as a verifiable sandbox to study different parallel sampling strategies in masked diffusion model

UniFFBench: Evaluating Universal Machine Learning Force Fields Against Experimental Measurements

arXiv:2508.05762v3 Announce Type: replace-cross Abstract: Universal machine learning force fields (UMLFFs) promise to revolutionize materials science by enabling rapid atomistic simulations across the

applicationsarxiv-cs-lg

Unlocking In-Context Learning in Audio-Language Models from Decentralized Medical Audio

arXiv:2606.23243v1 Announce Type: new Abstract: Clinical audio diagnosis in low-resource settings requires models that identify conditions from minimal examples without large annotated corpora. We pro

UNSEEN: Uncertainty-aware Navigation via Sparse Estimation in Unknown Environments

arXiv:2606.20755v1 Announce Type: new Abstract: Visual navigation in unknown environments remains a core challenge in mobile robotics, especially for resource-constrained platforms. Most existing appr

applicationsarxiv-cs-ro

Unsupervised Domain Adaptation for Sim-to-Real Object Pose Estimation with Contrastive Alignment and Pseudo-Label Refinement

arXiv:2606.21287v1 Announce Type: new Abstract: Unsupervised domain adaptation (UDA) enables robust transfer of knowledge from simulated to real environments while exploiting a subset of unlabeled tar

safetyarxiv-cs-cv

Unsupervised Susceptibility Distortion Correction of EPI without Calibration Scans via Image Translation-Based Registration

arXiv:2606.21588v1 Announce Type: cross Abstract: Functional magnetic resonance imaging (fMRI) utilizes echo-planar imaging (EPI) to capture blood-oxygen-level-dependent (BOLD) signals with high tempo

Agents

VDAWorld: World Modelling via VLM-Directed Abstraction and Simulation

arXiv:2512.11061v2 Announce Type: replace Abstract: Generative video models, a leading approach to world modelling, face fundamental limitations. They often violate physical and logical rules, lack in

agentsarxiv-cs-cv

Vera: A Layered Diffusion Model for Content-Preserving Video Editing

arXiv:2606.23610v1 Announce Type: new Abstract: Video diffusion models have enabled remarkable progress in video generation and editing. However, content preservation remains a core challenge: existin

VeriBound: PAC-Bayesian Generalization Bounds for Process Reward Models Trained with Formal Verification Tools

arXiv:2606.20740v1 Announce Type: cross Abstract: Process Reward Models (PRMs) provide step-level verification for Large Language Model (LLM) reasoning, yet their training data acquisition remains a b

VeriEvol: Scaling Multimodal Mathematical Reasoning via Verifiable Evol-Instruct

arXiv:2606.23543v1 Announce Type: cross Abstract: Scaling reinforcement learning for visual mathematical reasoning requires more than generating harder questions: as data volume grows, the reward labe

Visual Geometry Transformer in the Wild: Distractor-Free 3D Reconstruction

arXiv:2606.22787v1 Announce Type: new Abstract: Current end-to-end multi-view 3D reconstruction methods achieve impressive results, but rely on a restrictive static assumption: the scenes is entire di

applicationsarxiv-cs-cv

VLA-FAIL: Efficient Task Failure Detection for Finetuned Vision-Language-Action Models

arXiv:2606.21386v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) achieve state-of-the-art performance on many robotic manipulation tasks, yet they can still behave unpredictably

applicationsarxiv-cs-cv

VRPO: Rethinking Value Modeling for Robust RL under Noisy Supervision in LLM Post-Training

arXiv:2508.03058v2 Announce Type: replace Abstract: Reinforcement Learning (RL) in real-world environments often suffers from ambiguous or incomplete reward supervision, which undermines policy stabil

safetyarxiv-cs-lg