Demonstration of Pneuma-Seeker: Agentic System for Reifying and Fulfilling Information Needs on Tabular Data

2026-04-15

AI Summary

This work addresses the common challenge faced by data analysts who begin with vague questions and iteratively refine their information needs through exploration. To support this process, the authors propose Pneuma-Seeker, a novel system that leverages large language models (LLMs) not as opaque question-answering engines but as transparent, interactive analytical collaborators. Pneuma-Seeker enables users to explicitly articulate their information requirements as verifiable relational specifications and supports iterative refinement of these specifications, targeted data discovery, and execution with full provenance tracking. By integrating LLMs with relational specification modeling, data provenance, and an interactive interface, the system demonstrates its effectiveness in two real-world public procurement use cases, where it successfully helped users dynamically concretize evolving analytical needs and accurately retrieve relevant data.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Question Answering
• Search and Optimization: Distributed Search

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search
• Security and Privacy: Data transparency and provenance

Abstract

Data analysts working with relational data often start with vague or underspecified questions and refine them iteratively as they explore the data. To support this iterative process, we demonstrate Pneuma-Seeker, a system that reifies a user's information need as explicit, inspectable relational specifications, enabling iterative refinement of the information need, targeted data discovery, and provenance-aware execution. Through two real-world procurement use cases, we show how Pneuma-Seeker leverages LLMs as transparent, interactive analytical collaborators rather than opaque answer engines.

Problem

• information need
• tabular data
• iterative refinement
• relational data
• data exploration

Innovation

• agentic system
• relational specifications
• iterative refinement
• provenance-aware execution
• LLM as collaborator