---
name: journal-arxiv-ai-a-case-study-20260609
description: >
  学术期刊学习笔记：arXiv AI - A case study of evaluating AI agents on a neuroscience data-to-discovery pipelin
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version: 1.0.0
author: mundo-journal-bot
priority: LOW
auto_activate: MANUAL
category: journal-learnings
source: arXiv AI
published: Tue, 09 Jun 2026 00:00:00 -0400
learned: 2026-06-09
---
# A case study of evaluating AI agents on a neuroscience data-to-discovery pipeline

**来源**: [arXiv AI](https://arxiv.org/abs/2606.07718)
**学习日期**: 2026-06-09

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## 摘要

arXiv:2606.07718v1 Announce Type: new Abstract: Agentic AI tools offer a promising path to automating software development bottlenecks in scientific research pipelines, particularly for stages that take domain experts days to months to build, where scientists care about correctness and robustness, not implementation details. We present an empirical study of general-purpose coding agents on a fly optogenetics data-to-discovery pipeline. We assess agents on tasks substantially larger than existing

## 关键发现

1. 07718v1 Announce Type: new Abstract: Agentic AI tools offer a promising path to automating software development bottlenecks in scientific research pipelines, particularly for stages that take domain experts days to months to build, where scientists care about correctness and robustness, not implementation details
2. We present an empirical study of general-purpose coding agents on a fly optogenetics data-to-discovery pipeline

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