Paper Overview
- Research Area: AI/ML
- Authors: MasterControl AI Lab
- Published: 2026-09-06
- arXiv: 2509.00002
- The restriction remains expressive within a defined analytical class, using relational operations plus aggregation, comparison, windows, ranking, and similarity.
- Fixed meaning, policy, data, and execution rules make results replayable.
- 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.
Summary
The paper studies 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.
Key claims
Caveat
The authors note this is a result of a specific configuration and does not prove that runtime agents cannot succeed under other designs.
Original 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. 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. This is a ...
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