Tagged articles

Zero‑Shot Generalization

4 articles · Page 1 of 1

Jul 18, 2026 · Artificial Intelligence

World’s First Cloud‑Deployed Embodied AI Model Swaps Robotic Hands in 30 Seconds

Visics demonstrated the world’s first cloud‑based embodied AI model at WAIC 2026, showing a single brain controlling multiple robotic hands that can be swapped in 30 seconds without retraining, achieving 99% grasp success across ten hand types using a VLOA architecture and massive video‑simulation data.

Cloud DeploymentEaaSZero‑Shot Generalization

0 likes · 10 min read

World’s First Cloud‑Deployed Embodied AI Model Swaps Robotic Hands in 30 Seconds

SuanNi

May 31, 2026 · Artificial Intelligence

How NVIDIA’s Gamma‑World Turns Single‑Agent Models into Multiplayer Experiences

Gamma‑World introduces a multi‑agent world model that solves identity, interaction, and real‑time inference challenges with parameter‑free geometric encoding, sparse hub attention, and teacher‑student distillation, enabling zero‑shot generalization from two to four agents and achieving 24 FPS interactive video generation.

Gamma-WorldMulti-AgentReal-time inference

0 likes · 11 min read

How NVIDIA’s Gamma‑World Turns Single‑Agent Models into Multiplayer Experiences

AI Frontier Lectures

Dec 15, 2025 · Artificial Intelligence

How UnityVideo Unifies Multimodal Training to Boost Video Generation

UnityVideo, a new vision framework from HKUST, CUHK, Tsinghua and Kuaishou, unifies training across depth, flow, pose, segmentation and RGB modalities, achieving faster convergence, higher video quality, zero‑shot generalization and stronger physical reasoning compared with existing single‑modality video generators.

AI researchMultimodal Video GenerationUnityVideo

0 likes · 15 min read

How UnityVideo Unifies Multimodal Training to Boost Video Generation

AI Frontier Lectures

Jul 18, 2025 · Artificial Intelligence

How Anchored Attributes Boost Prompt Learning for Vision‑Language Models

The paper introduces ATPrompt, a method that inserts fixed attribute tokens into learnable prompts for CLIP‑style vision‑language models, enabling the soft prompts to capture generic attribute representations and significantly improve base‑to‑novel generalization without extra regularization losses.

ATPromptVision-Language ModelsZero‑Shot Generalization

0 likes · 20 min read

How Anchored Attributes Boost Prompt Learning for Vision‑Language Models