Autonomous research agents for mathematical conjecture testing: Bridging Stata 19 and Agentic AI

Prasad Kothari

Northern European Stata Conference 2026 from Stata Users Group

Abstract: As Large Language Models (LLMs) transition from generative chatbots to autonomous reasoning agents, the potential for automating complex scientific discovery workflows has expanded. This presentation introduces an agentic AI framework designed to solve and verify mathematical conjectures - such as those in extremal combinatorics and geometric structures - by leveraging Stata 19 as a primary engine for rigorous statistical validation and structural deep learning. This session demonstrates how an autonomous agent can: orchestrate workflows using the ReAct paradigm to decompose high-level mathematical hypotheses into executable Stata code; perform statistical verification by employing Stata's Bayesian variable selection (bayesselect) and Structural Equation Modeling (SEM) to test the stability of generated conjectures against large-scale synthetic datasets; and carry out topological data analysis (TDA) by integrating external Python-based TDA libraries with Stata's visualization tools to identify geometric patterns in mathematical objects. The session will include a "Tips and Tricks" segment on building "Statistics Agent Skills" - local markdown-based instruction sets that allow LLMs to maintain context-aware modeling strategies within the Stata environment. We demonstrate that by embedding Stata's rigorous econometric standards into an agent's "second brain," we can mitigate the logical reasoning failures common in generic LLMs while accelerating the pace of decentralized science.

Date: 2026-10-01
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Persistent link: https://EconPapers.repec.org/RePEc:boc:neur26:07

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