Institution profile

Prairie View A&M University

Academic institutionnorthamerica · us
Official website
Research library11linked papers
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Selected work

Representative Papers

Mind the Refinement Gap: When Safe High-Level Robot Plans Produce Unsafe Executions

Oct 01, 2026

This study addresses the safety verification gap in language-driven robotic systems, where high-level planning and safety monitoring overlook implicit action effects during execution. By auditing discrepancies between RoboGuard’s judgments on abstract plans and graph-refined trajectories, it reveals potential failures in tracking completeness assumptions. Methodologically, this work proposes a graph-based trajectory refinement mechanism serving as both a lightweight mitigation strategy and a diagnostic tool for physical AI safety, integrating semantic graph planning, Linear Temporal Logic (LTL) monitoring, and SPINE natural language instruction generation. Experimental results demonstrate that the predicted deviations were successfully reproduced across all 28 controlled cases, thereby validating the effectiveness of the proposed refinement approach.

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Recent Advances in Deep Learning-Based Drug-Target Binding Affinity Prediction

Aug 13, 2026

This study addresses the limitations in accuracy and evaluation of drug-target binding affinity prediction by systematically reviewing deep learning models and benchmark datasets. It identifies critical bottlenecks, including data bias, poor cold-start generalization, and the absence of standardized evaluation metrics. To overcome these challenges, this work proposes novel research paradigms encompassing standardized evaluation frameworks, improved dataset curation, and multimodal representation integration. By elucidating the core deficiencies of current methodologies, this paper establishes a future research roadmap prioritizing robustness and generalizability. Ultimately, this work provides essential theoretical foundations and methodological guidance to advance precision drug discovery, ensuring more reliable computational predictions in pharmaceutical development.

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UA-ChatDev: Uncertainty-Aware Multi-Agent Collaboration for Reliable Software Development

Jul 02, 2026

This work addresses the issue of hallucination propagation in existing large language model–driven multi-agent software development frameworks, which often stems from neglecting the reliability of intermediate outputs and ultimately degrades software quality. To mitigate this, the authors propose an uncertainty-aware multi-agent collaboration framework that quantifies response uncertainty through lightweight token-level log-probability estimation. By integrating phase-adaptive threshold calibration, the framework selectively triggers retrieval-augmented verification at high-risk stages, enabling reliable and context-sensitive collaboration. Evaluated on the SRDD benchmark, the proposed approach significantly outperforms current single- and multi-agent methods across key metrics, including completeness, executability, consistency, and overall software quality.

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Recent publications

Latest Papers

Mind the Refinement Gap: When Safe High-Level Robot Plans Produce Unsafe Executions

Oct 01, 2026

This study addresses the safety verification gap in language-driven robotic systems, where high-level planning and safety monitoring overlook implicit action effects during execution. By auditing discrepancies between RoboGuard’s judgments on abstract plans and graph-refined trajectories, it reveals potential failures in tracking completeness assumptions. Methodologically, this work proposes a graph-based trajectory refinement mechanism serving as both a lightweight mitigation strategy and a diagnostic tool for physical AI safety, integrating semantic graph planning, Linear Temporal Logic (LTL) monitoring, and SPINE natural language instruction generation. Experimental results demonstrate that the predicted deviations were successfully reproduced across all 28 controlled cases, thereby validating the effectiveness of the proposed refinement approach.

0 citationsRead paper

Recent Advances in Deep Learning-Based Drug-Target Binding Affinity Prediction

Aug 13, 2026

This study addresses the limitations in accuracy and evaluation of drug-target binding affinity prediction by systematically reviewing deep learning models and benchmark datasets. It identifies critical bottlenecks, including data bias, poor cold-start generalization, and the absence of standardized evaluation metrics. To overcome these challenges, this work proposes novel research paradigms encompassing standardized evaluation frameworks, improved dataset curation, and multimodal representation integration. By elucidating the core deficiencies of current methodologies, this paper establishes a future research roadmap prioritizing robustness and generalizability. Ultimately, this work provides essential theoretical foundations and methodological guidance to advance precision drug discovery, ensuring more reliable computational predictions in pharmaceutical development.

0 citationsRead paper

UA-ChatDev: Uncertainty-Aware Multi-Agent Collaboration for Reliable Software Development

Jul 02, 2026

This work addresses the issue of hallucination propagation in existing large language model–driven multi-agent software development frameworks, which often stems from neglecting the reliability of intermediate outputs and ultimately degrades software quality. To mitigate this, the authors propose an uncertainty-aware multi-agent collaboration framework that quantifies response uncertainty through lightweight token-level log-probability estimation. By integrating phase-adaptive threshold calibration, the framework selectively triggers retrieval-augmented verification at high-risk stages, enabling reliable and context-sensitive collaboration. Evaluated on the SRDD benchmark, the proposed approach significantly outperforms current single- and multi-agent methods across key metrics, including completeness, executability, consistency, and overall software quality.

0 citationsRead paper