# Orca：一个通用世界基础模型

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-29 08:00
- AIHOT 分数：52
- AIHOT 链接：https://aihot.virxact.com/items/cmr034hsc03f7slki7ajyhcmk
- 原文链接：https://arxiv.org/abs/2606.30534

## AI 摘要

Orca通过下一状态预测（Next-State-Prediction）统一建模多模态世界信号，学习统一的世界潜空间。预训练使用125K小时视频和1.6亿事件标注，包含无意识学习（连续视频中的密集自然状态转换）和有意识学习（语言描述事件和VQA监督下的稀疏状态转换）。冻结主干后，仅训练轻量级模态特定解码器，即可在文本生成、图像预测和具身动作生成三项下游任务上超越类似规模的专用基线模型。

## 正文

We introduce Orca, an initial instantiation of a general world foundation model. Orca learns a unified world latent space from multimodal world signals and exposes it through multimodal readout interfaces. Rather than optimizing isolated next-token, next-frame, or next-action prediction, we are centered on Next-State-Prediction modeling, offering a unified state-transition modeling route toward understanding, predicting, and acting upon the world. Orca learns through two complementary paradigms: unconscious learning captures dense natural state transitions from continuous videos, and conscious learning models sparse meaningful state transitions by language-described events and VQA supervision. For pre-training, we construct a large-scale world-learning inventory data, including 125K hours of video data and 160M event annotations. After pre-training, Orca learns a unified world latent space. To examine whether the learned latent supports downstream, we evaluate it by three representative downstream readouts: text generation, image prediction, and embodied action generation. Orca's backbone is frozen, and only the lightweight modality-specific decoders are trainable. Experiments show the scalability of the proposed paradigm and verify that stronger world latent enables stronger downstream readouts. Orca outperforms similar-sized specialized baselines. These results show that Orca, as a general world foundation model, presents a promising approach to understanding, predicting, and acting upon the world. Finally, we discuss the current limitations, aiming to provide useful insights and inspiration for the community.