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#Autoregressive (4)

WaterfallPagination
GaussianGPT Autoregressive 3D Generation
0:30

GaussianGPT Autoregressive 3D Generation

@rsasaki0109 · 172 views · 2026-08-29
GaussianGPTECCV3D Gaussian
G0.5: One Autoregressive Stream for Robot Reasoning and Action

G0.5: One Autoregressive Stream for Robot Reasoning and Action

G0.5 is a pretrained autoregressive VLA in which a single transformer decoder emits reasoning and action tokens under one objective. A cross-embodiment action tokenizer, a native chain-of-thought stream, and a visual memory module make this tractable at foundation-model scale, letting the pretrained VLM capabilities carry over to physical behavior. It surpasses pi-0.5 and GR00T-N1.7 across 7 regimes including real-world R1 robot fine-tuning, BEHAVIOR Challenge, DROID zero-shot transfer, LIBERO, RoboTwin 2.0 and SimplerEnv.

Yicheng Liu, Zibin Dong, Baijun YeAug 12, 2026
VLAGalaxeaFoundation ModelsAug 12, 2026
Galaxea G0.5: One Autoregressive VLA for Reasoning and Action
4:22

Galaxea G0.5: One Autoregressive VLA for Reasoning and Action

@robotsdigest · 136 views · 2026-08-13
VLAGalaxeaG0.5
Streaming Multi-Agent Autoregressive Diffusion Model with World State Registers

Streaming Multi-Agent Autoregressive Diffusion Model with World State Registers

Multi-agent interactive world models should not only generate consistent observations, but also maintain world states that persist across agents and evolve across views. Existing autoregressive video diffusion pipelines carry forward observation history as conditioning context, which makes shared state difficult to maintain in multi-agent and multi-view settings. We present WorldWeaver (W^2), a streaming multi-agent video diffusion model that augments rollout with cross-agent world state registers: learnable tokens that store shared world information, track individual agent status, and are dynamically updated after each generated chunk. We ground these registers with supervision signals spanning individual agent status, global state views including bird's-eye views, and scene text. We further improve the architecture with a Mixture-of-Transformers design that uses separate weights for world state modeling and visual frame modeling. Extensive experiments in two-agent Minecraft video generation show that explicit world-state modeling improves logical consistency and generation quality.

Sicheng Mo, Yuheng Li, Ziyang LengJul 23, 2026
World ModelsMulti-agentDiffusion modelsJul 23, 2026