Search: “synthesis”

HumanVBench: Probing Human-Centric Video Understanding in MLLMs with Automatically Synthesized Benchmarks

arXiv:2412.17574v3 Announce Type: replace-cross Abstract: Evaluating the nuanced human-centric video understanding capabilities of Multimodal Large Language Models (MLLMs) remains a great challenge, a

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Intra-finger Variability of Diffusion-based Latent Fingerprint Generation

DGX agent

arXiv:2604.10040v1 Announce Type: new Abstract: The primary goal of this work is to systematically evaluate the intra-finger variability of synthetic fingerprints (particularly latent prints) generate

researcharxiv-cs-cv

14 Apr 2026

Knowing What to Stress: A Discourse-Conditioned Text-to-Speech Benchmark

arXiv:2604.10580v1 Announce Type: new Abstract: Spoken meaning often depends not only on what is said, but also on which word is emphasized. The same sentence can convey correction, contrast, or clari

KSDiff: Keyframe-Augmented Speech-Aware Dual-Path Diffusion for Facial Animation

arXiv:2509.20128v2 Announce Type: replace-cross Abstract: Audio-driven facial animation has made significant progress in multimedia applications, with diffusion models showing strong potential for tal

Learning Long-term Motion Embeddings for Efficient Kinematics Generation

arXiv:2604.11737v1 Announce Type: new Abstract: Understanding and predicting motion is a fundamental component of visual intelligence. Although modern video models exhibit strong comprehension of scen

tutorialsarxiv-cs-cv

LIDEA: Human-to-Robot Imitation Learning via Implicit Feature Distillation and Explicit Geometry Alignment

arXiv:2604.10677v1 Announce Type: cross Abstract: Scaling up robot learning is hindered by the scarcity of robotic demonstrations, whereas human videos offer a vast, untapped source of interaction dat

LLMs for Text-Based Exploration and Navigation Under Partial Observability

arXiv:2604.09604v1 Announce Type: new Abstract: Exploration and goal-directed navigation in unknown layouts are central to inspection, logistics, and search-and-rescue. We ask whether large language m

MorphoFlow: Sparse-Supervised Generative Shape Modeling with Adaptive Latent Relevance

arXiv:2604.11636v1 Announce Type: new Abstract: Statistical shape modeling (SSM) is central to population level analysis of anatomical variability, yet most existing approaches rely on densely annotat

tutorialsarxiv-cs-cv

OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language World Models

arXiv:2604.10866v1 Announce Type: new Abstract: AI agents are expected to perform professional work across hundreds of occupational domains (from emergency department triage to nuclear reactor safety

Online Learning-Enhanced High Order Adaptive Safety Control

arXiv:2511.19651v2 Announce Type: replace Abstract: Control barrier functions (CBFs) are an effective model-based tool to formally certify the safety of a system. With the growing complexity of modern

Pioneer Agent: Continual Improvement of Small Language Models in Production

arXiv:2604.09791v1 Announce Type: new Abstract: Small language models are attractive for production deployment due to their low cost, fast inference, and ease of specialization. However, adapting them

Prompt Relay: Inference-Time Temporal Control for Multi-Event Video Generation

arXiv:2604.10030v1 Announce Type: new Abstract: Video diffusion models have achieved remarkable progress in generating high-quality videos. However, these models struggle to represent the temporal suc

Agents

Representations Before Pixels: Semantics-Guided Hierarchical Video Prediction

arXiv:2604.11707v1 Announce Type: new Abstract: Accurate future video prediction requires both high visual fidelity and consistent scene semantics, particularly in complex dynamic environments such as

agentsarxiv-cs-cv

RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs in Medicine

arXiv:2410.13987v3 Announce Type: replace Abstract: Answering complex real-world questions in the medical domain often requires accurate retrieval from medical Textual Knowledge Graphs (medical TKGs),

Ro-SLM: Onboard Small Language Models for Robot Task Planning and Operation Code Generation

arXiv:2604.10929v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) provide robots with contextual reasoning abilities to comprehend human instructions. Yet, current LLM-en

tutorialsarxiv-cs-ro

RoboStereo: Dual-Tower 4D Embodied World Models for Unified Policy Optimization

arXiv:2603.12639v2 Announce Type: replace Abstract: Scalable Embodied AI faces fundamental constraints due to prohibitive costs and safety risks of real-world interaction. While Embodied World Models

Robust Real-Time Coordination of CAVs: A Distributed Optimization Framework under Uncertainty

arXiv:2508.21322v2 Announce Type: replace Abstract: Achieving both safety guarantees and real-time performance in cooperative vehicle coordination remains a fundamental challenge, particularly in dyna

Seeing Through Deception: Uncovering Misleading Creator Intent in Multimodal News with Vision-Language Models

arXiv:2505.15489v4 Announce Type: replace-cross Abstract: The impact of multimodal misinformation arises not only from factual inaccuracies but also from the misleading narratives that creators delibe

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation

arXiv:2305.09958v5 Announce Type: replace Abstract: Graph neural networks (GNNs) realize great success in graph learning but suffer from performance loss when meeting heterophily, i.e. neighboring nod

researcharxiv-cs-lg

Simply retrieving a reasoning trace looks a lot like human reasoning, until it's time to navigate uncharted territory. If you memorized all …

researchfrancois-chollet--x

Simply retrieving a reasoning trace looks a lot like human reasoning, until it's time to navigate uncharted territory. If you memorized all reasoning traces of humans from 10,000 BC, you could automat

StaMo: Unsupervised Learning of Generalizable Robot Motion from Compact State Representation

arXiv:2510.05057v2 Announce Type: replace-cross Abstract: A fundamental challenge in embodied intelligence is developing expressive and compact state representations for efficient world modeling and d

Switch-JustDance: Benchmarking Whole Body Motion Tracking Controllers Using a Commercial Console Game

model-releasesarxiv-cs-cv

arXiv:2511.17925v3 Announce Type: replace-cross Abstract: Recent advances in whole-body robot control have enabled humanoid and legged robots to perform increasingly agile and coordinated motions. How

The Phantom of PCIe: Constraining Generative Artificial Intelligences for Practical Peripherals Trace Synthesizing

arXiv:2411.06376v3 Announce Type: replace-cross Abstract: Peripheral Component Interconnect Express (PCIe) is the de facto interconnect standard for high-speed peripherals and CPUs. The development of

UDAPose: Unsupervised Domain Adaptation for Low-Light Human Pose Estimation

arXiv:2604.10485v1 Announce Type: cross Abstract: Low-visibility scenarios, such as low-light conditions, pose significant challenges to human pose estimation due to the scarcity of annotated low-ligh

ViserDex: Visual Sim-to-Real for Robust Dexterous In-hand Reorientation

arXiv:2604.11138v1 Announce Type: cross Abstract: In-hand object reorientation requires precise estimation of the object pose to handle complex task dynamics. While RGB sensing offers rich semantic cu

VLMaterial: Vision-Language Model-Based Camera-Radar Fusion for Physics-Grounded Material Identification

arXiv:2604.11671v1 Announce Type: cross Abstract: Accurate material recognition is a fundamental capability for intelligent perception systems to interact safely and effectively with the physical worl

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models

arXiv:2604.11351v1 Announce Type: new Abstract: Imitation learning is a powerful paradigm for training robotic policies, yet its performance is limited by compounding errors: minor policy inaccuracies

Local Ai

Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials

arXiv:2604.08744v1 Announce Type: cross Abstract: The discovery of new energetic materials is critical for advancing technologies from defense to private industry. However, experimental approaches rem

local-aiarxiv-cs-lg

Agents

ActivityEditor: Learning to Synthesize Physically Valid Human Mobility

arXiv:2604.05529v2 Announce Type: replace Abstract: Human mobility modeling is indispensable for diverse urban applications. However, existing data-driven methods often suffer from data scarcity, limi

agentsarxiv-cs-ai

AVA-VLA: Improving Vision-Language-Action models with Active Visual Attention

arXiv:2511.18960v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have shown remarkable progress in embodied tasks recently, but most methods process visual observations in

ChipSeek: Optimizing Verilog Generation via EDA-Integrated Reinforcement Learning

arXiv:2507.04736v2 Announce Type: replace Abstract: Large Language Models have emerged as powerful tools for automating Register-Transfer Level (RTL) code generation, yet they face critical limitation

CT-1: Vision-Language-Camera Models Transfer Spatial Reasoning Knowledge to Camera-Controllable Video Generation

arXiv:2604.09201v1 Announce Type: new Abstract: Camera-controllable video generation aims to synthesize videos with flexible and physically plausible camera movements. However, existing methods either

tutorialsarxiv-cs-cv

FDIF: Formula-Driven supervised Learning with Implicit Functions for 3D Medical Image Segmentation

arXiv:2603.23199v2 Announce Type: replace Abstract: Deep learning-based 3D medical image segmentation methods relies on large-scale labeled datasets, yet acquiring such data is difficult due to privac

researcharxiv-cs-cv

From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models

arXiv:2604.09459v1 Announce Type: new Abstract: Reinforcement learning (RL) for large language models (LLMs) increasingly relies on sparse, outcome-level rewards -- yet determining which actions withi

GAN-Enhanced Deep Reinforcement Learning for Semantic-Aware Resource Allocation in 6G Network Slicing

arXiv:2604.08576v1 Announce Type: cross Abstract: Sixth-generation (6G) wireless networks must support heterogeneous services: enhanced Mobile Broadband (eMBB) requiring 1 Tbps data rates, massive Mac

Generative Simulation for Policy Learning in Physical Human-Robot Interaction

arXiv:2604.08664v1 Announce Type: new Abstract: Developing autonomous physical human-robot interaction (pHRI) systems is limited by the scarcity of large-scale training data to learn robust robot beha

Large-Scale Universal Defect Generation: Foundation Models and Datasets

arXiv:2604.08915v1 Announce Type: cross Abstract: Existing defect/anomaly generation methods often rely on few-shot learning, which overfits to specific defect categories due to the lack of large-scal

Local Ai

LTX distilled lora 1.1 vs. 1.0 comparison

local-air-stablediffusion

This r/StableDiffusion post presents a community-driven side-by-side comparison of the LTX Video model's distilled LoRA versions 1.1 and 1.0, evaluating differences in output quality, motion coherence

Multimodal Anomaly Detection for Human-Robot Interaction

arXiv:2604.09326v1 Announce Type: cross Abstract: Ensuring safety and reliability in human-robot interaction (HRI) requires the timely detection of unexpected events that could lead to system failures

Neural Distribution Prior for LiDAR Out-of-Distribution Detection

arXiv:2604.09232v1 Announce Type: cross Abstract: LiDAR-based perception is critical for autonomous driving due to its robustness to poor lighting and visibility conditions. Yet, current models operat

R2G: A Multi-View Circuit Graph Benchmark Suite from RTL to GDSII

model-releasesarxiv-cs-cv

arXiv:2604.08810v1 Announce Type: new Abstract: Graph neural networks (GNNs) are increasingly applied to physical design tasks such as congestion prediction and wirelength estimation, yet progress is

SAGE: A Service Agent Graph-guided Evaluation Benchmark

arXiv:2604.09285v1 Announce Type: new Abstract: The development of Large Language Models (LLMs) has catalyzed automation in customer service, yet benchmarking their performance remains challenging. Ex

Scene-Agnostic Object-Centric Representation Learning for 3D Gaussian Splatting

arXiv:2604.09045v1 Announce Type: new Abstract: Recent works on 3D scene understanding leverage 2D masks from visual foundation models (VFMs) to supervise radiance fields, enabling instance-level 3D s

Agents

SkillForge: Forging Domain-Specific, Self-Evolving Agent Skills in Cloud Technical Support

arXiv:2604.08618v1 Announce Type: cross Abstract: Deploying LLM-powered agents in enterprise scenarios such as cloud technical support demands high-quality, domain-specific skills. However, existing s

agentsarxiv-cs-ai

Structured Exploration and Exploitation of Label Functions for Automated Data Annotation

arXiv:2604.08578v1 Announce Type: cross Abstract: High-quality labeled data is critical for training reliable machine learning and deep learning models, yet manual annotation remains costly and error-

Applications

SynDocDis: A Metadata-Driven Framework for Generating Synthetic Physician Discussions Using Large Language Models

arXiv:2604.08555v1 Announce Type: new Abstract: Physician-physician discussions of patient cases represent a rich source of clinical knowledge and reasoning that could feed AI agents to enrich and eve

applicationsarxiv-cs-cl

Traj2Action: A Co-Denoising Framework for Trajectory-Guided Human-to-Robot Skill Transfer

arXiv:2510.00491v3 Announce Type: replace-cross Abstract: Learning diverse manipulation skills for real-world robots is severely bottlenecked by the reliance on costly and hard-to-scale teleoperated d

WAND: Windowed Attention and Knowledge Distillation for Efficient Autoregressive Text-to-Speech Models

arXiv:2604.08558v1 Announce Type: cross Abstract: Recent decoder-only autoregressive text-to-speech (AR-TTS) models produce high-fidelity speech, but their memory and compute costs scale quadratically