ZETA 架构将路由策略与视觉执行分离，提升科学图像分析智能体可审计性
Route First, See Later: Enabling Vision Agents to Reason Scientifically by Separating Routing Policy from Action Spaces
一篇很硬核的智能体架构论文：把路由和执行拆开，Qwen 3.8 的分割路由从 6.2% 拉到 75%，做科学图像自动化的值得看看思路。
论文提出 ZETA，一种把意图路由与视觉工具执行解耦的科学图像分析智能体架构，让路由错误与执行错误可以分别评估。在 51 条零样本科学意图查询的基准上，显式路由策略规范显著提升路由准确率：Qwen 3.8 的分割路由准确率从 6.2% 提升到 75.0%，检测路由准确率从 60.0% 提升到 100%。ZETA 将测量视为基于显式几何的确定性操作，并输出可分别观测的路由决策与执行痕迹。
Route First, See Later: Enabling Vision Agents to Reason Scientifically by Separating Routing Policy from Action Spaces
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.
