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Iowa State University

Academic institutionnorthamerica · us
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Research library430linked papers
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Selected work

Representative Papers

Affirmative Action in India: Restricted Strategy Space, Complex Constraints, and Direct Mechanism Design

Oct 04, 2023Social Science Research Network

This paper addresses resource allocation imbalances under India’s multi-layered reservation system—combining vertical (social group-based) and horizontal (cross-cutting criteria, e.g., gender, disability) quotas—amid practical constraints including quota rollback conflicts, restricted preference expression, and deeply nested priority structures. We introduce the Generalized Lexicographic (GL) family of selection rules, the first formal framework unifying legally mandated hierarchical priorities across reservation layers. Integrating a deferred-acceptance algorithm with a law-mechanism co-design architecture, we propose a direct matching mechanism that is constitutionally compliant, strategy-proof, and fair. It guarantees full utilization of reserved positions and significantly improves substantive representation of disadvantaged subgroups—including women and persons with disabilities—in education and public employment. Our mechanism offers a scalable, legally grounded paradigm for multidimensional affirmative action policy design.

4 citationsRead paper

HiDVFS: A Hierarchical Multi-Agent DVFS Scheduler for OpenMP DAG Workloads

Jan 10, 2026arXiv.org

Existing DVFS scheduling approaches struggle to achieve fine-grained, coordinated optimization of performance, energy consumption, and thermal behavior for OpenMP DAG tasks during parallel execution. This work proposes HiDVFS, a hierarchical multi-agent reinforcement learning scheduler that introduces, for the first time, a hierarchical multi-agent architecture to DVFS. HiDVFS employs three cooperative agents to jointly manage task-core-frequency assignment, temperature-aware core-set selection, and task prioritization under resource contention. By integrating task profiling data, real-time thermal feedback, and a makespan-oriented reward function augmented with energy-efficiency and thermal regularization terms, HiDVFS achieves, on the NVIDIA Jetson TX2 platform using the BOTS benchmark suite, an average speedup of 3.95× and 47.1% energy reduction compared to GearDVFS, with peak improvements reaching 3.44× speedup and 50.4% energy savings.

3 citationsRead paper

QualityFlow: An Agentic Workflow for Program Synthesis Controlled by LLM Quality Checks

Jan 20, 2025arXiv.org

This work addresses the insufficient correctness guarantees in program synthesis by proposing a collaborative synthesis framework integrating dynamic multi-agent workflows with an LLM-based quality checker. Methodologically, it establishes a closed-loop collaboration among code generation, test execution, and self-debugging agents, and introduces the first LLM quality checker that explicitly models program execution traces to assess test compliance in real time—enabling dynamic submission, issue clarification, and step-level backtracking—augmented by diverse prompting and quality-feedback-driven adaptive decision-making. The key contribution is the first integration of dynamic execution-aware quality verification into the synthesis pipeline, enabling fine-grained procedural control. Empirically, the approach achieves state-of-the-art performance on MBPP, HumanEval, and EvalPlus, significantly outperforming static workflows and zero-shot one-shot synthesis baselines.

3 citationsRead paper

ZeroDVFS: Zero-Shot LLM-Guided Core and Frequency Allocation for Embedded Platforms

Jan 13, 2026

This work addresses the limitations of traditional dynamic voltage and frequency scaling (DVFS) and task-core allocation methods, which rely on heuristics or offline profiling, struggle to generalize to unseen workloads, and neglect stall time—leading to suboptimal energy efficiency and thermal management. To overcome these challenges, the paper proposes the first large language model (LLM)-guided, zero-shot multi-agent reinforcement learning framework for runtime scheduling. The approach leverages an LLM to extract 13-dimensional code-level semantic features from OpenMP programs and integrates hierarchical multi-agent action decomposition, regression-based environment modeling, and a Dyna-Q architecture to enable workload-agnostic scheduling without prior profiling. Experiments on Jetson TX2/Orin NX, RubikPi, and Intel Core i7 platforms demonstrate a 7.09× improvement in energy efficiency and a 4× reduction in task completion time compared to the Linux ondemand scheduler, with the first scheduling decision made 8,300× faster than conventional tabular methods.

2 citationsRead paper

A Family-Based Approach to Safety Cases for Controlled Airspaces in Small Uncrewed Aerial Systems

Jul 27, 2024AIAA AVIATION FORUM AND ASCEND 2024

To address safety violations caused by frequent unauthorized incursions of small Unmanned Aircraft Systems (sUAS) into controlled airspace and the inefficiency of manual safety assurance, this paper proposes SafeSPLE—a novel approach that pioneers the application of Software Product Line Engineering (SPLE) to safety case development. SafeSPLE integrates hazard analysis with feature modeling to construct a parameterized safety case template; domain-specific safety claims are then automatically instantiated and generated via product line configuration tailored to individual flight missions. This enables customizable, scalable, and regulation-compliant airspace access control while significantly improving assessment consistency and efficiency. Empirical evaluation demonstrates that SafeSPLE efficiently produces regulatory-compliant safety cases, offering a reusable, verifiable technical foundation for sUAS integration into controlled airspace.

2 citationsRead paper
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