论文/arXiv 2610.21882·13 天前

Anvil：通过知识再循环扩展具身空间智能

Anvil通过知识循环统一数据生成、训练与评测，提升具身空间智能，并在七个公开基准上验证。

AI 整理

• 本体 SDK 与控制
• 端侧部署
• 评测与安全

原文链接：Anvil: Scaling Embodied Spatial Intelligence through Knowledge Recirculation（arXiv 2610.21882 · 发布日期：2026-10-15）

Anvil: Scaling Embodied Spatial Intelligence through Knowledge Recirculation

Anvil通过知识循环统一数据生成、训练与评测，提升具身空间智能，并在七个公开基准上验证。

核心要点

• 动机是具身空间智能需定位实体、推理关系并表达为行动相关形式，而联合训练易混淆不同阶段知识。
• Anvil-Gen以异构数据飞轮结合形式特定生成、跨阶段一致性验证和失败驱动再收集，覆盖11种Ground--Relation--Act形式。
• Anvil-Ben提供场景对齐监督与场景关联评估，以区分阶段能力和错误传播。
• Anvil-MOPD通过专家教师蒸馏整合异构空间知识，在七个公开基准平均优于Qwen3.5-9B和RynnBrain-8B。

验证范围与局限：验证范围为基于图像的空间记录、保留评测集以及七个公开基准；摘要未说明真机实验。

英文摘要 (Abstract)

Embodied spatial intelligence requires more than visual recognition: models must localize entities, reason about their relations, and express this knowledge in action-relevant forms. Yet joint training can conflate the knowledge needed at different stages because supervision is fragmented across incompatible geometric and semantic outputs. We introduce Anvil, a unified framework for data generation, training, and benchmarking for embodied spatial intelligence. (1) Anvil-Gen is a heterogeneous data flywheel that combines form-specific generation, cross-stage consistency verification, and failure-driven recollection across 11 Ground--Relation--Act forms. (2) Anvil-Ben provides scene-aligned supervision for learning diverse embodied spatial capabilities and scene-linked assessments that distinguish stage competence from error propagation. (3) Anvil-MOPD is a 9B student that consolidates heterogeneous spatial knowledge through distillation from specialist teachers. Semantic-first-fault routing targets teacher supervision to the student's earliest decisive failure, while stage normalization controls the loss scale across native outputs of different lengths. We construct 5.8M source-grounded records spanning 160K images, reserving 580K spatial records for held-out evaluation and using the remaining 5.22M records for training. On average across seven public benchmarks, Anvil-MOPD outperforms Qwen3.5-9B and RynnBrain-8B by 9.29 and 11.03 percentage points, respectively, while reaching 80.67% on VABench-Point. Controlled studies support targeted coverage, selective transfer, and feedback-guided repair as complementary framework components.

作者：Weilin Ruan, Bohan Zhou, Jinghao Li, Yangkai Wei, Wenqian Li, Peiran Dong, Yingjie Xu, Leyi Wu, Haojian Huang, Binghui Xie

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.21882：Anvil: Scaling Embodied Spatial Intelligence through Knowledge Recirculation访问 2026-10-21