Large Language Models
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Shell-Based Agents as Execution Systems: A Survey of Command Generation, Verification, Security, State, and Recovery
Abstract: Shell environments are increasingly becoming interfaces through which AI agents interact with software and operating systems. Unlike conventional command generation systems, shell-based agents can interpret task objectives, plan multi-step actions, invoke terminal tools, observe system state, and adapt subsequent actions. These capabilities support software development, system administration, experimentation, and automated operations, but also introduce reliability and security challenges that cannot be assessed through command generation alone. This survey reviews 57 …
Published in Journal of Advances in Shell Programming · Vol. 13, Issue 2, 2026 · pp. 30–54 Read article
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Reward-Aligned Reinforcement Learning from Human Feedback for Emotion-Sensitive Large Language Model Therapists: Balancing Empathetic Engagement, Boundary Safety, and Clinical Accountability
Abstract: The deployment of large language models (LLMs) in mental health therapy contexts introduces a critical alignment challenge: these systems must simultaneously cultivate genuine empathic rapport, observe clinically grounded safety boundaries, and remain auditable under institutional accountability frameworks. Existing reinforcement learning from human feedback (RLHF) pipelines optimize for a scalar reward signal that is demonstrably insufficient for the multi-objective, temporally extended nature of therapeutic conversation. This paper presents RA-RLHF-T (Reward-Aligned RLHF …
Published in International Journal of Tropical Medicines · Vol. 3, Issue 2, 2026 Read article
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Text-to-SQL Systems: A Comprehensive Study of Evolution, Architectural Advancements, Challenges, and the Role of Large Language Models in Intelligent Query Generation
Abstract: Interacting with relational databases traditionally requires knowledge of Structured Query Language (SQL), which creates a significant barrier for users who do not have technical expertise. To address this challenge, Text-to-SQL systems have emerged as an important area of research, enabling users to express their information needs in natural language while automatically generating executable SQL queries. These systems aim to make database access more intuitive, efficient, and accessible across a wide …
Published in Journal of Advanced Database Management & Systems · Vol. 13, Issue 2, 2026 · pp. 10–19 Read article
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AI-Powered Prompt-to-Production Platform for Automated React Application Generation and Cloud Deployment
Abstract: The proliferation of large language models (LLMs) has fundamentally altered the landscape of automated software engineering. However, a significant “deployment chasm” persists between the generation of code artifacts and their realization as production-grade cloud applications. Traditional workflows are marred by the “DevOps Tax”: the high operational overhead of environment configuration, dependency resolution, and infrastructure provisioning. This paper presents a novel, technically adept architecture that bridges this gap through a unified …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 13, Issue 2, 2026 · pp. 1–16 Read article