arXiv cs.AI· Shubhabrata Mukherjee, Jeffrey Donatelli, Daniela Ushizima·· 11 小时前AI 评分34
ZETA 架构：将路由策略与动作空间分离，让视觉智能体科学推理
Route First, See Later: Enabling Vision Agents to Reason Scientifically by Separating Routing Policy from Action Spaces
AI 导读
ZETA 架构将路由策略与动作执行解耦，先解析科学意图再调用专用视觉工具，使路由正确性与下游执行可作为独立失败模式评估。在 51 条科学意图查询的零样本基准上，显式路由策略规范使 Qwen 3.8 的分割路由准确率从 6.2% 提升至 75.0%，检测路由准确率从 60.0% 提升至 100%。执行轨迹让路由决策与下游产物可分别观测，从而区分意图选择错误与视觉执行失败。
正文 · 原文
Abstract:Scientific image-analysis workflows require agents to select among diverse capabilities like description, detection, counting, and segmentation. Current multi-tool architectures conflate intent interpretation with visual execution, masking whether a failure arises from flawed reasoning (misrouting) or failed perception (invalid tool execution). This lack of auditability renders agents unreliable for high-stakes scientific automation. We introduce ZETA, an extensible architecture that operationalizes the decoupling of routing policy from action execution. ZETA resolves scientific intent before invoking specialized visual tools, treating measurement as a deterministic operation grounded in explicit geometry rather than generative output. This architecture exposes the "black box" of agent decision-making, allowing routing correctness and downstream execution to be evaluated as independent failure modes.
To our knowledge, we provide the first controlled study in scientific vision-agent routing that isolates explicit routing-policy specification while holding the legal action interface fixed. Evaluation on a zero-shot benchmark of 51 scientific-intent queries shows that explicit routing semantics can substantially improve routing accuracy, particularly at semantically adjacent action boundaries, although improvements vary across models. For Qwen 3.8, explicit routing-policy specification improves segmentation routing accuracy from 6.2% to 75.0% and detection routing accuracy from 60.0% to 100%. ZETA's execution traces make routing decisions and downstream artifacts separately observable, providing a basis for distinguishing intent-selection errors from visual execution failures. ZETA establishes the necessary infrastructure to isolate execution errors from interpretive ones, a critical requirement for deploying reliable scientific vision agents.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.06760 [cs.CV] |
| (or arXiv:2611.06760v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06760 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shubhabrata Mukherjee [view email]
[v1]
Wed, 4 Nov 2026 16:39:17 UTC (6,478 KB)
来源：arXiv cs.AI · arxiv.org