Institution profile

Universidade Federal de Ouro Preto

Academic institutionsouthamerica · br
Official website
Research library17linked papers
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Selected work

Representative Papers

SemPlan: Benchmarking Structured Semantic Planning for LLM-Based Queries over Enterprise Data

Aug 12, 2026

This study addresses semantic ambiguity, policy violations, and result uncertainty in enterprise data querying by constructing a bilingual synthetic benchmark to systematically evaluate four LLM architectures. Through structured semantic planning, a deterministic execution engine, and paired correctness analysis, we reveal that structural constraints modulate failure mechanisms rather than monotonically improving performance. Experiments demonstrate that the A3 architecture achieves the highest accuracy (25.67%), A1 excels in compliance, and A4 offers the lowest cost, with a stable subset repetition rate of 98.67%. These findings elucidate critical trade-offs among correctness, safety, and operational cost, providing empirical foundations for designing reliable Natural Language Query systems.

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An Adaptive KKT-Based Indicator for Convergence Assessment in Multi-Objective Optimization

Mar 04, 2026

This work proposes a reference-set-free adaptive convergence metric for multi-objective optimization that addresses the scalability limitations of existing indicators when the true Pareto front is unknown. By leveraging the Karush–Kuhn–Tucker (KKT) optimality conditions, the method integrates an entropy-inspired stationarity measure with a quantile normalization mechanism to enhance robustness against heterogeneous residual distributions. While preserving the intrinsic interpretability of KKT-based analysis, the proposed metric significantly improves stability and applicability in both many-objective and high-dimensional scenarios, thereby overcoming the scalability bottlenecks inherent in conventional convergence indicators.

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Recent publications

Latest Papers

SemPlan: Benchmarking Structured Semantic Planning for LLM-Based Queries over Enterprise Data

Aug 12, 2026

This study addresses semantic ambiguity, policy violations, and result uncertainty in enterprise data querying by constructing a bilingual synthetic benchmark to systematically evaluate four LLM architectures. Through structured semantic planning, a deterministic execution engine, and paired correctness analysis, we reveal that structural constraints modulate failure mechanisms rather than monotonically improving performance. Experiments demonstrate that the A3 architecture achieves the highest accuracy (25.67%), A1 excels in compliance, and A4 offers the lowest cost, with a stable subset repetition rate of 98.67%. These findings elucidate critical trade-offs among correctness, safety, and operational cost, providing empirical foundations for designing reliable Natural Language Query systems.

0 citationsRead paper

An Adaptive KKT-Based Indicator for Convergence Assessment in Multi-Objective Optimization

Mar 04, 2026

This work proposes a reference-set-free adaptive convergence metric for multi-objective optimization that addresses the scalability limitations of existing indicators when the true Pareto front is unknown. By leveraging the Karush–Kuhn–Tucker (KKT) optimality conditions, the method integrates an entropy-inspired stationarity measure with a quantile normalization mechanism to enhance robustness against heterogeneous residual distributions. While preserving the intrinsic interpretability of KKT-based analysis, the proposed metric significantly improves stability and applicability in both many-objective and high-dimensional scenarios, thereby overcoming the scalability bottlenecks inherent in conventional convergence indicators.

0 citationsRead paper