EviSeek：以证据为中心的具身主动信息获取智能体框架
EviSeek: An Evidence-Centric Agentic Framework for Embodied Active Information Seeking
AI 导读
EviSeek 是一个免训练框架，将问题衍生的证据需求显式转化为获取目标，指导具身智能体通过视角变化和物理交互有目的地收集证据。
正文 · 原文
Abstract:Active embodied question answering requires agents to acquire evidence through viewpoint changes and physical interaction. The central challenge is to avoid undirected interaction: taking plausible actions without specifying what evidence they should reveal for answering the question. Action plans and accumulated histories alone can leave the informational purpose of an interaction implicit. We introduce EviSeek, a training-free framework that makes question-derived evidence demands explicit acquisition objectives, linking observation-conditioned actions and selectively retained evidence to the information the agent seeks. On 3,436 ESI-Bench questions, EviSeek improves accuracy over the Single-model agent by 7.1 percentage points with Qwen3-VL-32B and 3.7 points with Gemini 3.1 Pro, while improving visual-support-weighted accuracy and reducing mean token usage. These findings support making the informational purpose of interaction explicit, with action selection and evidence retention organized around what the agent needs to learn to answer the question.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.06434 [cs.AI] |
| (or arXiv:2611.06434v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06434 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jintao Li [view email]
[v1]
Wed, 4 Nov 2026 12:35:04 UTC (5,704 KB)
来源：arXiv cs.AI · arxiv.org