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

Muhammad Imam Luthfi Balaka; Raul Castro Fernandez

cs.AIApr 15, 2026

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

Authors: Muhammad Imam Luthfi Balaka, Raul Castro Fernandez

Organizations: The University of Chicago, Chicago, USA

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.

Figures & tables

Figure 1. Architecture of Pneuma-Seeker

Panel of Figure 2 — Figure 2. Demonstration of Pneuma-Seeker on Two Use Cases

Data Analysis Agents

Data analysis agents use language models and tools to explore datasets, run analyses, and produce answers or reports grounded in evidence.

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May 18, 2026J/K move · Enter open · S save

PIPER: Content-Based Table Search via profiling and LLM-Generated Pseudoqueries

Riccardo Terrenzi, Matteo Falconi, Serkan Ayvaz, Pierluigi Plebani

Centre for Industrial Software, University of Southern Denmark, Sønderborg, Denmark · Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy

Information Retrieval Table Retrieval

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The rapid growth of tabular datasets in data lakes, data spaces, and open data portals makes effective dataset search essential for reuse and analysis. Existing search systems rely mainly on metadata, which is often incomplete or low quality, especially for tables whose meaning depends on both schema and cell values. Recent advances in Large Language Models (LLMs) enable richer, content-based representations of tables. However, prior LLM-based retrieval methods have focused on Table Question Answering, where the goal is to select a single table to answer a question, rather than retrieve and rank relevant datasets. We propose PIPER, a content-driven retrieval method for tabular datasets that uses table profiles and LLM-generated queries embedded for dense retrieval. Designed for dataset search in poor-metadata settings, PIPER outperforms both classical metadata-based baselines and strong TableQA retrieval methods, demonstrating the value of LLM-based content modeling for tabular dataset search.

Abstract

Data Analysis Agents

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