Problem
Research questions and friction points this paper is trying to address.
computational notebook
human-agent interaction
contextual understanding
visual and computational context
Point, Revise, Review: Grounded Agentic Analysis in Reactive Notebooks with marimo-lens
🤖 AI Summary
本文通过marimo-lens扩展程序解决人与代理在计算笔记本中交流时上下文丢失的问题，使人能直观地标记输出并获得相关计算背景。
📝 Abstract
In a computational notebook, a human can point to a rendered result and ask about "this," while an agent acts through cells, dependencies, and runtime state. When what the human sees and what the agent operates on are disconnected, the human must describe what they mean and trace what the agent did in prose that strips away situated visual and computational context. We present marimo-lens, an extension to the reactive Python notebook marimo for grounded human-agent analysis. Lens connects a human's marked output and note to its producing cell and relevant contributing computation, giving the agent computational context for the request. It surfaces agent activity, returns selected results to the notebook, and preserves the initiating selection for human review and reopening. We illustrate the lifecycle through an exploratory human-agent analysis of a real-world open dataset, showing how visually situated questions lead to computational inspection, notebook action, and returned evidence for human review.
Innovation
Methods, ideas, or system contributions that make the work stand out.
marimo-lens
grounded human-agent analysis
computational context
reactive notebooks
visual and computational context
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