# VLA是否还记得基础知识？衡量视觉-语言-动作模型中常识与世界知识的保留程度

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-17 08:00
- AIHOT 分数：53
- AIHOT 链接：https://aihot.virxact.com/items/cmr2ahrgh06axsl8zqx4dmv6q
- 原文链接：https://arxiv.org/abs/2606.19297

## AI 摘要

为评估视觉-语言-动作模型微调后是否保留常识与事实知识，研究提出Act2Answer轻量协议，将知识评测转为动作答题：智能体通过单次物体放置动作选择答案，获得低控制偏差的动作接地成功率。在涵盖多种常识类别的测试集上，对7个VLA模型与9个VLM基线进行排名。结果显示，VLA在简单概念上表现稳健，但在语义丰富类别上与源VLM差距较大；VQA共训练与更好知识保留相关；答案相关信号在VLA中层最强，上层衰减。

## 正文

Embodied Vision-Language-Action (VLA) models are typically obtained by fine-tuning powerful pretrained VLMs on robotics data, yet it is unclear how much commonsense and factual knowledge they retain after adaptation. Failures on knowledge-sensitive tasks are ambiguous, conflating missing knowledge with poor generalization of low-level control. We introduce Act2Answer, a lightweight protocol that adapts VLM knowledge benchmarks to VLA evaluation by requiring agents to answer through action. Each question becomes a short tabletop episode where the agent performs a single object-placement action to select among candidate answers, yielding an action-grounded success rate with reduced control confounds. We curate a test suite of such environments across diverse commonsense and world-knowledge categories and introduce layerwise intent probing to localize answer-relevant information across the VLM backbone and action head. In a large-scale study of 7 VLA models and 9 VLM baselines, we systematically rank models across categories, finding that VLAs show solid performance on simple concepts while exhibiting larger gaps on richer semantic categories relative to their source VLMs, that VQA co-training is associated with better knowledge retention, and that answer-relevant signals peak in middle VLA layers but attenuate in upper layers. Act2Answer is available at https://tttonyalpha.github.io/act2answer/.