# ForkSCOPE: Charting the Agentic Garden of Forking Paths

arXiv: https://arxiv.org/abs/2609.12438 · HTML: https://mlsanity.com/paper/2609.12438/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 11 Sep 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 16 of 16)

- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation](https://mlsanity.com/paper/1802.00682/) (2018)
- [Veridical Data Science](https://mlsanity.com/paper/1901.08152/) (2019)
- [Selective Explanations: Leveraging Human Input to Align Explainable AI](https://mlsanity.com/paper/2301.09656/) (2023)
- [Improving Steering and Verification in AI-Assisted Data Analysis with Interactive Task Decomposition](https://mlsanity.com/paper/2407.02651/) (2024)
- [How Do Analysts Understand and Verify AI-Assisted Data Analyses?](https://mlsanity.com/paper/2309.10947/) (2023)
- [How Do Data Analysts Respond to AI Assistance? A Wizard-of-Oz Study](https://mlsanity.com/paper/2309.10108/) (2023)
- [LightVA: Lightweight Visual Analytics with LLM Agent-Based Task Planning and Execution](https://mlsanity.com/paper/2411.05651/) (2024)
- [Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review](https://mlsanity.com/paper/2310.19778/) (2023)
- [Paths Explored, Paths Omitted, Paths Obscured: Decision Points & Selective Reporting in End-to-End Data Analysis](https://mlsanity.com/paper/1910.13602/) (2019)
- [Does AI help humans make better decisions? A statistical evaluation framework for experimental and observational studies](https://mlsanity.com/paper/2403.12108/) (2024)
- [Many AI Analysts, One Dataset: Navigating the Agentic Data Science Multiverse](https://mlsanity.com/paper/2602.18710/) (2026)
- [Decision-Making Under Uncertainty in Research Synthesis: Designing for the Garden of Forking Paths](https://mlsanity.com/paper/1901.02957/) (2019)
- [Boba: Authoring and Visualizing Multiverse Analyses](https://mlsanity.com/paper/2007.05551/) (2020)
- [Sanity Checks for Agentic Data Science](https://mlsanity.com/paper/2604.11003/) (2026)
- [The Agentic Garden of Forking Paths](https://mlsanity.com/paper/2607.01507/) (2026)

## Abstract (arXiv)

Even with a fixed dataset and research question, data analysis involves many defensible decisions. Understanding how these choices influence the results is scientifically important but remains challenging. Crowdsourcing and agentic AI can generate hundreds of end-to-end analyses, but scaling generation alone can create a processing bottleneck and an analytic ``black hole.'' A common workaround is to impose a shared fixed decision taxonomy, which can limit insight and understate uncertainty. We present ForkSCOPE, a human-AI collaboration framework that induces structure bottom-up from the code corpus of end-to-end analyses, without a taxonomy fixed before or after generation, so the organization and evaluation of the garden can scale with the corpus. ForkSCOPE surfaces the charted garden of forking paths through a human-AI collaboration pipeline and an evidence-linked interactive viewer for steering and verification: it spotlights organically identified forks and structures and produces a derived taxonomy and decision map compatible with existing multiverse tools.