Abstract & Executive Summary
The paper introduces a novel governed enterprise analytics framework involving language models interpreting intents and deterministic policy-execution to generate SQL queries and execute pre-approved analytical programs. The method is tested across four hundred runs with three large-scale 8 billion-parameter transformers, demonstrating expressiveness within defined classes and replayability of results without violating the full answer-and-evidence contract.
Theoretical Foundation & Fundamental Principles
Key principles include interpretive language models, deterministic policy execution, SQL generation, analytical program selection, and data management. Underlying mathematical and biological mechanisms involve probabilistic interpretation by models, logical decision-making in policies, database operations, statistical aggregation, and comparison-based reasoning. The framework is constrained within relational algebraic operations and can handle complex queries with windows, ranking, and similarity measures.
Research Breakthrough & Experimental Findings
The breakthrough demonstrates that under defined analytical classes, governed enterprise analytics remains expressive and replayable. It achieves this by interpreting intent in language models, executing policy-driven programs for SQL generation and execution, ensuring results are both informative (answer-and-evidence) and verifiable through predefined rules and data access. The study evaluates three 8 billion-parameter transformers across various test datasets and finds that while runtime-planning episodes sometimes do not perfectly match the full answer-and-evidence contract, policy-executed analyzers consistently meet this requirement.
Primary Research Attribution & Source Credits
Authors: T. Chen, M. Liu, J. Wang
Joint University of Science and Technology (JUST), China
Nature Machine Intelligence
Abstract & Executive Summary
The paper introduces a novel governed enterprise analytics framework involving language models interpreting intents and deterministic policy-execution to generate SQL queries and execute pre-approved analytical programs. The method is tested across four hundred runs with three large-scale 8 billion-parameter transformers, demonstrating expressiveness within defined classes and replayability of results without violating the full answer-and-evidence contract.
Theoretical Foundation & Fundamental Principles
Key principles include interpretive language models, deterministic policy execution, SQL generation, analytical program selection, and data management. Underlying mathematical and biological mechanisms involve probabilistic interpretation by models, logical decision-making in policies, database operations, statistical aggregation, and comparison-based reasoning. The framework is constrained within relational algebraic operations and can handle complex queries with windows, ranking, and similarity measures.
Research Breakthrough & Experimental Findings
The breakthrough demonstrates that under defined analytical classes, governed enterprise analytics remains expressive and replayable. It achieves this by interpreting intent in language models, executing policy-driven programs for SQL generation and execution, ensuring results are both informative (answer-and-evidence) and verifiable through predefined rules and data access. The study evaluates three 8 billion-parameter transformers across various test datasets and finds that while runtime-planning episodes sometimes do not perfectly match the full answer-and-evidence contract, policy-executed analyzers consistently meet this requirement.
Primary Research Attribution & Source Credits
Authors: T. Chen, M. Liu, J. Wang
Joint University of Science and Technology (JUST), China
Nature Machine Intelligence
Abstract & Executive Summary
The paper introduces a novel governed enterprise analytics framework involving language models interpreting intents and deterministic policy-execution to generate SQL queries and execute pre-approved analytical programs. The method is tested across four hundred runs with three large-scale 8 billion-parameter transformers, demonstrating expressiveness within defined classes and replayability of results without violating the full answer-and-evidence contract.
Theoretical Foundation & Fundamental Principles
Key principles include interpretive language models, deterministic policy execution, SQL generation, analytical program selection, and data management. Underlying mathematical and biological mechanisms involve probabilistic interpretation by models, logical decision-making in policies, database operations, statistical aggregation, and comparison-based reasoning. The framework is constrained within relational algebraic operations and can handle complex queries with windows, ranking, and similarity measures.
Research Breakthrough & Experimental Findings
The breakthrough demonstrates that under defined analytical classes, governed enterprise analytics remains expressive and replayable. It achieves this by interpreting intent in language models, executing policy-driven programs for SQL generation and execution, ensuring results are both informative (answer-and-evidence) and verifiable through predefined rules and data access. The study evaluates three 8 billion-parameter transformers across various test datasets and finds that while runtime-planning episodes sometimes do not perfectly match the full answer-and-evidence contract, policy-executed analyzers consistently meet this requirement.
Primary Research Attribution & Source Credits
Authors: T. Chen, M. Liu, J. Wang
Joint University of Science and Technology (JUST), China
Nature Machine Intelligence
Abstract & Executive Summary
The paper introduces a novel governed enterprise analytics framework involving language models interpreting intents and deterministic policy-execution to generate SQL queries and execute pre-approved analytical programs. The method is tested across four hundred runs with three large-scale 8 billion-parameter transformers, demonstrating expressiveness within defined classes and replayability of results without violating the full answer-and-evidence contract.
Theoretical Foundation & Fundamental Principles
Key principles include interpretive language models, deterministic policy execution, SQL generation, analytical program selection, and data management. Underlying mathematical and biological mechanisms involve probabilistic interpretation by models, logical decision-making in policies, database operations, statistical aggregation, and comparison-based reasoning. The framework is constrained within relational algebraic operations and can handle complex queries with windows, ranking, and similarity measures.
Research Breakthrough & Experimental Findings
The breakthrough demonstrates that under defined analytical classes, governed enterprise analytics remains expressive and replayable. It achieves this by interpreting intent in language models, executing policy-driven programs for SQL generation and execution, ensuring results are both informative (answer-and-evidence) and verifiable through predefined rules and data access. The study evaluates three 8 billion-parameter transformers across various test datasets and finds that while runtime-planning episodes sometimes do not perfectly match the full answer-and-evidence contract, policy-executed analyzers consistently meet this requirement.
Primary Research Attribution & Source Credits
Authors: T. Chen, M. Liu, J. Wang
Joint University of Science and Technology (JUST), China
Nature Machine Intelligence
Abstract & Executive Summary
The paper introduces a novel governed enterprise analytics framework involving language models interpreting intents and deterministic policy-execution to generate SQL queries and execute pre-approved analytical programs. The method is tested across four hundred runs with three large-scale 8 billion-parameter transformers, demonstrating expressiveness within defined classes and replayability of results without violating the full answer-and-evidence contract.
Theoretical Foundation & Fundamental Principles
Key principles include interpretive language models, deterministic policy execution, SQL generation, analytical program selection, and data management. Underlying mathematical and biological mechanisms involve probabilistic interpretation by models, logical decision-making in policies, database operations, statistical aggregation, and comparison-based reasoning. The framework is constrained within relational algebraic operations and can handle complex queries with windows, ranking, and similarity measures.
Research Breakthrough & Experimental Findings
The breakthrough demonstrates that under defined analytical classes, governed enterprise analytics remains expressive and replayable. It achieves this by interpreting intent in language models, executing policy-driven programs for SQL generation and execution, ensuring results are both informative (answer-and-evidence) and verifiable through predefined rules and data access. The study evaluates three 8 billion-parameter transformers across various test datasets and finds that while runtime-planning episodes sometimes do not perfectly match the full answer-and-evidence contract, policy-executed analyzers consistently meet this requirement.
Primary Research Attribution & Source Credits
Authors: T. Chen, M. Liu, J. Wang
Joint University of Science and Technology (JUST), China
Nature Machine Intelligence
Abstract & Executive Summary
The paper introduces a novel governed enterprise analytics framework involving language models interpreting intents and deterministic policy-execution to generate SQL queries and execute pre-approved analytical programs. The method is tested across four hundred runs with three large-scale 8 billion-parameter transformers, demonstrating expressiveness within defined classes and replayability of results without violating the full answer-and-evidence contract.
Theoretical Foundation & Fundamental Principles
Key principles include interpretive language models, deterministic policy execution, SQL generation, analytical program selection, and data management. Underlying mathematical and biological mechanisms involve probabilistic interpretation by models, logical decision-making in policies, database operations, statistical aggregation, and comparison-based reasoning. The framework is constrained within relational algebraic operations and can handle complex queries with windows, ranking, and similarity measures.
Research Breakthrough & Experimental Findings
The breakthrough demonstrates that under defined analytical classes, governed enterprise analytics remains expressive and replayable. It achieves this by interpreting intent in language models, executing policy-driven programs for SQL generation and execution, ensuring results are both informative (answer-and-evidence) and verifiable through predefined rules and data access. The study evaluates three 8 billion-parameter transformers across various test datasets and finds that while runtime-planning episodes sometimes do not perfectly match the full answer-and-evidence contract, policy-executed analyzers consistently meet this requirement.
Primary Research Attribution & Source Credits
Authors: T. Chen, M. Liu, J. Wang
Joint University of Science and Technology (JUST), China
Nature Machine Intelligence
Abstract & Executive Summary
The paper introduces a novel governed enterprise analytics framework involving language models interpreting intents and deterministic policy-execution to generate SQL queries and execute pre-approved analytical programs. The method is tested across four hundred runs with three large-scale 8 billion-parameter transformers, demonstrating expressiveness within defined classes and replayability of results without violating the full answer-and-evidence contract.
Theoretical Foundation & Fundamental Principles
Key principles include interpretive language models, deterministic policy execution, SQL generation, analytical program selection, and data management. Underlying mathematical and biological mechanisms involve probabilistic interpretation by models, logical decision-making in policies, database operations, statistical aggregation, and comparison-based reasoning. The framework is constrained within relational algebraic operations and can handle complex queries with windows, ranking, and similarity measures.
Research Breakthrough & Experimental Findings
The breakthrough demonstrates that under defined analytical classes, governed enterprise analytics remains expressive and replayable. It achieves this by interpreting intent in language models, executing policy-driven programs for SQL generation and execution, ensuring results are both informative (answer-and-evidence) and verifiable through predefined rules and data access. The study evaluates three 8 billion-parameter transformers across various test datasets and finds that while runtime-planning episodes sometimes do not perfectly match the full answer-and-evidence contract, policy-executed analyzers consistently meet this requirement.
Primary Research Attribution & Source Credits
Authors: T. Chen, M. Liu, J. Wang
Joint University of Science and Technology (JUST), China
Nature Machine Intelligence
Abstract & Executive Summary
The paper introduces a novel governed enterprise analytics framework involving language models interpreting intents and deterministic policy-execution to generate SQL queries and execute pre-approved analytical programs. The method is tested across four hundred runs with three large-scale 8 billion-parameter transformers, demonstrating expressiveness within defined classes and replayability of results without violating the full answer-and-evidence contract.
Theoretical Foundation & Fundamental Principles
Key principles include interpretive language models, deterministic policy execution, SQL generation, analytical program selection, and data management. Underlying mathematical and biological mechanisms involve probabilistic interpretation by models, logical decision-making in policies, database operations, statistical aggregation, and comparison-based reasoning. The framework is constrained within relational algebraic operations and can handle complex queries with windows, ranking, and similarity measures.
Research Breakthrough & Experimental Findings
The breakthrough demonstrates that under defined analytical classes, governed enterprise analytics remains expressive and replayable. It achieves this by interpreting intent in language models, executing policy-driven programs for SQL generation and execution, ensuring results are both informative (answer-and-evidence) and verifiable through predefined rules and data access. The study evaluates three 8 billion-parameter transformers across various test datasets and finds that while runtime-planning episodes sometimes do not perfectly match the full answer-and-evidence contract, policy-executed analyzers consistently meet this requirement.
Primary Research Attribution & Source Credits
Authors: T. Chen, M. Liu, J. Wang
Joint University of Science and Technology (JUST), China
Nature Machine Intelligence
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