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🔍 program development 📂 AI & Data Science
Showing 31717 results for "program development" in AI & Data Science
AI & Data Science Preprint PDF

From Stochastic to Deterministic: A Multi-Criteria Decision Analysis Framework for Bounded Semantic Parsing in AI-Driven Recruitment Screening

A. H. Syed · 2026

The tension between automation and accuracy sits at the heart of modern talent acquisition. Recruiters need swiftness. Organisations need secure, auditable decisions. And candidates—often talented ind…

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AI & Data Science Preprint PDF DOI

Intern-Atlas: A Methodological Evolution Graph as Research Infrastructure for AI Scientists

Yujun Wu, Dongxu Zhang, Xinchen Li, Jinhang Xu, Yiling Duan, Yumou Liu, Jiabao Pan, Xuanhe Zhou, Jingxuan Wei, Siyuan Li, Jintao Chen, Conghui He, Cheng Tan · 2026

Existing research infrastructure is fundamentally document-centric, providing citation links between papers but lacking explicit representations of methodological evolution. In particular, it does not…

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AI & Data Science Preprint PDF DOI

FiLMMeD: Feature-wise Linear Modulation for Cross-Problem Multi-Depot Vehicle Routing

Arthur Correa, Paulo Nascimento, Samuel Moniz · 2026

Solving practical multi-depot vehicle routing problems (MDVRP) is a challenging optimization task central to modern logistics, increasingly driven by e-commerce. To address the MDVRP's computational c…

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AI & Data Science Preprint PDF DOI

3D Reconstruction Techniques in the Manufacturing Domain: Applications, Research Opportunities and Use Cases

Chialoon Cheng, Kaijun liu, Zhiyang Liu, Marcelo H Ang Jr · 2026

This comprehensive review examines the evolution and the current state of the art in three-dimensional (3D) reconstruction techniques in manufacturing applications. The analysis covers both traditiona…

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Collaborative Agent Reasoning Engineering (CARE): A Three-Party Design Methodology for Systematically Engineering AI Agents with Subject Matter Experts, Developers, and Helper Agents

Rahul Ramachandran, Nidhi Jha, Muthukumaran Ramasubramanian · 2026

We present Collaborative Agent Reasoning Engineering (CARE), a disciplined methodology for engineering Large Language Model (LLM) agents in scientific domains. Unlike ad-hoc trial-and-error approaches…

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AI & Data Science Preprint PDF DOI

Rethinking Agentic Reinforcement Learning In Large Language Models

Fangming Cui, Ruixiao Zhu, Cheng Fang, Sunan Li, Jiahong Li · 2026

Reinforcement Learning (RL) has traditionally focused on training specialized agents to optimize predefined reward functions within narrowly defined environments. However, the advent of powerful Large…

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Optimal allocation of trials to sub-regions in crop variety testing with multiple years and correlated genotype effects

Maryna Prus, Lenka Filova, Hans-Peter Piepho, Waqas Ahmed Malik · 2026

Plant breeding and variety trials are usually conducted in multiple environments sampled from a defined target population of environments in order to characterize the performance of breeding lines or …

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On the Expressive Power of GNNs to Solve Linear SDPs

Chendi Qian, Christopher Morris · 2026

Semidefinite programs (SDPs) are a powerful framework for convex optimization and for constructing strong relaxations of hard combinatorial problems. However, solving large SDPs can be computationally…

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GourNet: A CNN-Based Model for Mango Leaf Disease Detection

Ekram Alam, Jaydip Sanyal, Akhil Kumar Das, Arijit Bhattacharya, Farhana Sultana · 2026

Mango cultivation is crucial in the agricultural sector, significantly contributing to economic development and food security. However, diseases affecting mango leaves can significantly reduce both th…

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Language Ideologies in a Multilingual Society: An LLM-based Analysis of Luxembourgish News Comments

Emilia Milano, Alistair Plum, Yves Scherrer, Christoph Purschke · 2026

Detecting language ideologies is a valuable yet complex task for understanding how identities are constructed through discourse. In Luxembourg's multicultural and multilingual society, language ideolo…

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Green Physics-Informed Machine Learning Models For Structural Health Monitoring

Daisy R Bradley, Elizabeth J Cross · 2026

Machine learning continues to emerge as an important tool to be utilised within structural engineering and structural health monitoring, due to its ability to accurately and quickly perform both regre…

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Privacy-Preserving Federated Learning via Differential Privacy and Homomorphic Encryption for Cardiovascular Disease Risk Modeling

Gaurang Sharma, Juha Pajula, Aada Illikainen, Markus Rautell, Noora Lipsonen, Petri Alhainen, Mika Hilvo · 2026

Protecting sensitive health data while enabling collaborative analysis is a central challenge in healthcare. Traditional machine learning (ML) requires institutions to pool anonymized patient records,…

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Beyond the Training Distribution: Mapping Generalization Boundaries in Neural Program Synthesis

Henrik Voigt, Michael Habeck, Joachim Giesen · 2026

Large-scale transformers achieve impressive results on program synthesis benchmarks, yet their true generalization capabilities remain obscured by data contamination and opaque training corpora. To ri…

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Skills-Coach: A Self-Evolving Skill Optimizer via Training-Free GRPO

Yu Tian, Jiawei Chen, Lifan Zheng, Mingxiang Tao, Xinyi Zeng, Zhaoxia Yin, Hang Su, Xian Sun · 2026

We introduce Skills-Coach, a novel automated framework designed to significantly enhance the self-evolution of skills within Large Language Model (LLM)-based agents. Addressing the current fragmentati…

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InteractWeb-Bench: Can Multimodal Agent Escape Blind Execution in Interactive Website Generation?

Qiyao Wang, Haoran Hu, Longze Chen, Hongbo Wang, Hamid Alinejad-Rokny, Yuan Lin, Min Yang · 2026

With the advancement of multimodal large language models (MLLMs) and coding agents, the website development has shifted from manual programming to agent-based project-level code synthesis. Existing be…

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CoAX: Cognitive-Oriented Attribution eXplanation User Model of Human Understanding of AI Explanations

Louth Bin Rawshan, Zhuoyu Wang, Brian Y. Lim · 2026

Explainable AI (XAI) aims to improve user understanding and decisions when using AI models. However, despite innovations in XAI, recent user evaluations reveal that this goal remains elusive. Understa…

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Investigating More Explainable and Partition-Free Compositionality Estimation for LLMs: A Rule-Generation Perspective

Ziyao Xu, Cong Wang, Houfeng Wang · 2026

Compositional generalization tests are often used to estimate the compositionality of LLMs. However, such tests have the following limitations: (1) they only focus on the output results without consid…

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Inference on Generalized Latent Variable Models with High-Dimensional Responses and Covariates

Jing Ouyang, Chengyu Cui, Yunxiao Chen, Kean Ming Tan, Gongjun Xu · 2026

Regression models with both high-dimensional responses and covariates have attracted growing attention. Standard multivariate regression models become inadequate when the response variables depend not…

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The Two Boundaries: Why Behavioral AI Governance Fails Structurally

Alan L. McCann · 2026

Every system that performs effects has two boundaries: what it can do (expressiveness) and what governance covers (governance). In nearly all deployed AI systems, these boundaries are defined independ…

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Mechanized Foundations of Structural Governance: Machine-Checked Proofs for Governed Intelligence

Alan L. McCann · 2026

We present five results in the theory of structural governance for cognitive workflow systems. Three are mechanized in Coq 8.19 using the Interaction Trees library with parameterized coinduction; two …

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