Nomad: Autonomous Exploration and Discovery

arXiv cs.AI / 4/1/2026

📰 NewsSignals & Early TrendsIdeas & Deep AnalysisModels & Research

共有:

Key Points

The article introduces Nomad, an autonomous system for exploration-first data discovery that aims to uncover insights beyond what users can explicitly frame in advance.
Nomad builds an explicit Exploration Map, traverses it to balance breadth and depth, and uses an explorer agent with document/web search and database tools to generate and investigate hypotheses.
It adds quality control by using an independent verifier before sending candidate insights into a reporting pipeline that produces cited reports and higher-level meta-reports.
The work proposes an evaluation framework for autonomous discovery systems that assesses trustworthiness, report quality, and diversity, and reports improved results on a corpus of UN and WHO materials versus baseline approaches.
Overall, the system is positioned as a step toward autonomous research that can determine which questions and directions are worth surfacing, not just answer pre-specified queries.

Abstract

We introduce Nomad, a system for autonomous data exploration and insight discovery. Given a corpus of documents, databases, or other data sources, users rarely know the full set of questions, hypotheses, or connections that could be explored. As a result, query-driven question answering and prompt-driven deep-research systems remain limited by human framing and often fail to cover the broader insight space. Nomad addresses this problem with an exploration-first architecture. It constructs an explicit Exploration Map over the domain and systematically traverses it to balance breadth and depth. It generates and selects hypotheses and investigates them with an explorer agent that can use document search, web search, and database tools. Candidate insights are then checked by an independent verifier before entering a reporting pipeline that produces cited reports and higher-level meta-reports. We also present a comprehensive evaluation framework for autonomous discovery systems that measures trustworthiness, report quality, and diversity. Using a corpus of selected UN and WHO reports, we show that omad{} produces more trustworthy and higher-quality reports than baselines, while also producing more diverse insights over several runs. Nomad is a step toward autonomous systems that not only answer user questions or conduct directed research, but also discover which questions, research directions, and insights are worth surfacing in the first place.