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MasterControl Seventeen Every Time: Governed LLM Analytics with Policy-Executed Programs

Forum topic · 小凯 · 2026-09-06

Summary

This arXiv paper (2509.00002) from MasterControl AI Lab studies a governed approach to enterprise analytics in which a language model interprets the user's question while deterministic policy selects and runs a pre-approved analytical program, returning both results and evidence. The authors show this restriction remains expressive within a defined analytical class, using relational operations plus aggregation, comparison, windows, ranking, and similarity. Fixed semantic, policy, data, and execution rules also make results replayable. In 440 runs, three 8B models generated SQL and selected tools at runtime, while Qwen3-8B interpreted intent only and policy executed the approved program. None of 330 runtime-planning episodes matched the full answer-and-evidence contract across all test datasets; the policy-executed analyzer matched 110 of 110. The authors note this reflects their specific configuration and does not prove runtime agents cannot succeed under other designs.

Paper Overview

Field: AI/ML Authors: MasterControl AI Lab Published: 2026-09-06 arXiv: 2509.00002

Abstract

We study a governed approach to enterprise analytics: a language model interprets the question, while deterministic policy selects and runs a pre-approved analytical program that returns both results and evidence. We show that this restriction can remain expressive within a defined analytical class, using relational operations plus aggregation, comparison, windows, ranking, and similarity. Fixed meaning, policy, data, and execution rules also make results replayable.

Key Findings

  • Across 440 runs, three 8B models generated SQL and selected tools at runtime, while Qwen3-8B interpreted intent only and policy executed the approved program.
  • None of 330 runtime-planning episodes matched the full answer-and-evidence contract across all test datasets.
  • The policy-executed analyzer matched 110 of 110 runs.
  • The authors note this is a result of the specific configuration and does not prove that runtime agents cannot succeed under other designs.
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Tags

#ai#machine-learning#llm#enterprise-analytics#governance#text-to-sql#arxiv-paper

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