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NAAMII

Research institution
Research library5linked papers
Opportunities0open roles
Selected work

Representative Papers

iARCS: Iterative Agentic RL for Controllable 3D Scene Generation

Aug 06, 2026

Existing 3D scene generation methods struggle to reliably satisfy task-critical functional constraints such as navigability and reachability, limiting the practical utility of synthetic data. This work proposes an iterative agent-based reinforcement learning framework that first enhances physical plausibility and layout quality through pretraining with generic rewards, then leverages a large language model (LLM) to generate executable, task-specific reward programs. These LLM-generated rewards are integrated into a feedback-driven reinforcement learning loop for iterative refinement. By uniquely combining LLM-synthesized reward functions with iterative reinforcement learning, the approach significantly improves adherence to functional constraints while preserving scene diversity, thereby enhancing downstream task performance.

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The AI Pyramid A Conceptual Framework for Workforce Capability in the Age of AI

Jan 10, 2026arXiv.org

Existing AI literacy frameworks struggle to address the systemic disruption generative AI poses to high-skill white-collar work and lack a robust framework for human–AI collaboration. This study proposes the “AI Pyramid” model, introducing the novel concept of “AI-native competencies” and reclassifying human capabilities into three tiers: AI-native, AI-foundational, and AI-advanced. Moving beyond traditional occupational hierarchies, this model reconceptualizes the societal distribution of skills at a systemic level. Through conceptual modeling, competency ontology design, scenario-based problem-based learning (PBL), and competency-oriented assessment, the study establishes a scalable pathway for cultivating an AI-ready workforce. The framework offers actionable strategies for educational institutions, enterprises, and governments to enhance societal productivity and resilience while mitigating technology-driven inequality.

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From Development to Deployment of AI-assisted Telehealth and Screening for Vision- and Hearing-threatening diseases in resource-constrained settings: Field Observations, Challenges and Way Forward

Sep 18, 2025

In resource-limited settings, early screening for ophthalmic and otologic diseases is hindered by critical shortages of specialists, inadequate diagnostic equipment, and the difficulty of transitioning paper-based workflows to AI-ready digital systems. Method: This study proposes an end-to-end, iterative co-design methodology that tightly integrates AI model development—including transfer learning and automated image quality assessment—with digital health workflow reengineering. Field-based prototyping, shadow deployment, and continuous feedback loops were employed to rigorously evaluate system usability and operational feasibility. Contribution/Results: We introduce a novel “AI–Workflow–Human Factors” triadic framework for localized adaptation, distilling reusable deployment insights and evidence-based strategies for overcoming key implementation barriers—such as workflow misalignment, clinician trust deficits, and infrastructure constraints. The resulting dual-dimensional (technical and managerial) guidance framework enables sustainable, scalable deployment of AI-assisted screening programs in low-resource environments.

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Recent publications

Latest Papers

iARCS: Iterative Agentic RL for Controllable 3D Scene Generation

Aug 06, 2026

Existing 3D scene generation methods struggle to reliably satisfy task-critical functional constraints such as navigability and reachability, limiting the practical utility of synthetic data. This work proposes an iterative agent-based reinforcement learning framework that first enhances physical plausibility and layout quality through pretraining with generic rewards, then leverages a large language model (LLM) to generate executable, task-specific reward programs. These LLM-generated rewards are integrated into a feedback-driven reinforcement learning loop for iterative refinement. By uniquely combining LLM-synthesized reward functions with iterative reinforcement learning, the approach significantly improves adherence to functional constraints while preserving scene diversity, thereby enhancing downstream task performance.

0 citationsRead paper

The AI Pyramid A Conceptual Framework for Workforce Capability in the Age of AI

Jan 10, 2026arXiv.org

Existing AI literacy frameworks struggle to address the systemic disruption generative AI poses to high-skill white-collar work and lack a robust framework for human–AI collaboration. This study proposes the “AI Pyramid” model, introducing the novel concept of “AI-native competencies” and reclassifying human capabilities into three tiers: AI-native, AI-foundational, and AI-advanced. Moving beyond traditional occupational hierarchies, this model reconceptualizes the societal distribution of skills at a systemic level. Through conceptual modeling, competency ontology design, scenario-based problem-based learning (PBL), and competency-oriented assessment, the study establishes a scalable pathway for cultivating an AI-ready workforce. The framework offers actionable strategies for educational institutions, enterprises, and governments to enhance societal productivity and resilience while mitigating technology-driven inequality.

0 citationsRead paper

From Development to Deployment of AI-assisted Telehealth and Screening for Vision- and Hearing-threatening diseases in resource-constrained settings: Field Observations, Challenges and Way Forward

Sep 18, 2025

In resource-limited settings, early screening for ophthalmic and otologic diseases is hindered by critical shortages of specialists, inadequate diagnostic equipment, and the difficulty of transitioning paper-based workflows to AI-ready digital systems. Method: This study proposes an end-to-end, iterative co-design methodology that tightly integrates AI model development—including transfer learning and automated image quality assessment—with digital health workflow reengineering. Field-based prototyping, shadow deployment, and continuous feedback loops were employed to rigorously evaluate system usability and operational feasibility. Contribution/Results: We introduce a novel “AI–Workflow–Human Factors” triadic framework for localized adaptation, distilling reusable deployment insights and evidence-based strategies for overcoming key implementation barriers—such as workflow misalignment, clinician trust deficits, and infrastructure constraints. The resulting dual-dimensional (technical and managerial) guidance framework enables sustainable, scalable deployment of AI-assisted screening programs in low-resource environments.

0 citationsRead paper