# Querying Everything Everywhere All at Once: Supervaluationism for the Agentic Lakehouse

arXiv: https://arxiv.org/abs/2603.13380 · HTML: https://mlsanity.com/paper/2603.13380/

## 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 8 of its 27 references that are in the map.
- First posted on arXiv 11 Mar 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, 8 of 8)

- [Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL](https://mlsanity.com/paper/2406.08426/) (2024)
- [Supporting Our AI Overlords: Redesigning Data Systems to be Agent-First](https://mlsanity.com/paper/2509.00997/) (2025)
- [Building a serverless Data Lakehouse from spare parts](https://mlsanity.com/paper/2308.05368/) (2023)
- [Safe, Untrusted, "Proof-Carrying" AI Agents: toward the agentic lakehouse](https://mlsanity.com/paper/2510.09567/) (2025)
- [Reproducible data science over data lakes: replayable data pipelines with Bauplan and Nessie](https://mlsanity.com/paper/2404.13682/) (2024)
- [Trustworthy AI in the Agentic Lakehouse: from Concurrency to Governance](https://mlsanity.com/paper/2511.16402/) (2025)
- [Building a Correct-by-Design Lakehouse. Data Contracts, Versioning, and Transactional Pipelines for Humans and Agents](https://mlsanity.com/paper/2602.02335/) (2026)
- [Categorizing Online Shopping Behavior from Cosmetics to Electronics: An Analytical Framework](https://mlsanity.com/paper/2010.02503/) (2020)

## Abstract (arXiv)

Agentic analytics is turning the lakehouse into a multi-version system: swarms of (human or AI) producers materialize competing pipelines in data branches, while (human or AI) consumers need answers without knowing the underlying data life-cycle. We demonstrate a new system that answers questions across branches rather than at a single snapshot. Our prototype focuses on a novel query path that evaluates queries under supervaluationary semantics. In the absence of comparable multi-branch querying capabilities in mainstream OLAP systems, we open source the demo code as a concrete baseline for the OLAP community.