Why we think this paper is great for you:
This paper directly addresses the development and operationalization of advanced AI models, including LLMs, which is central to your interests. It explores integrated environments for building and monitoring the very systems you are interested in.

Abstract
The rapid expansion of artificial intelligence and machine learning (ML) applications has intensified the demand for integrated environments that unify model development, deployment, and monitoring. Traditional Integrated Development Environments (IDEs) focus primarily on code authoring, lacking intelligent support for the full ML lifecycle, while existing MLOps platforms remain detached from the coding workflow. To address this gap, this study proposes the design of an LLM-Integrated IDE with automated MLOps pipelines that enables continuous model development and monitoring within a single environment. The proposed system embeds a Large Language Model (LLM) assistant capable of code generation, debugging recommendation, and automatic pipeline configuration. The backend incorporates automated data validation, feature storage, drift detection, retraining triggers, and CI/CD deployment orchestration. This framework was implemented in a prototype named SmartMLOps Studio and evaluated using classification and forecasting tasks on the UCI Adult and M5 datasets. Experimental results demonstrate that SmartMLOps Studio reduces pipeline configuration time by 61%, improves experiment reproducibility by 45%, and increases drift detection accuracy by 14% compared to traditional workflows. By bridging intelligent code assistance and automated operational pipelines, this research establishes a novel paradigm for AI engineering - transforming the IDE from a static coding tool into a dynamic, lifecycle-aware intelligent platform for scalable and efficient model development.

AI Summary

• The proposed system enhances experiment reproducibility by 45% and increases drift detection accuracy by 14% compared to traditional workflows, demonstrating improved reliability in dynamic ML environments. [3]
• Experimental validation on UCI Adult and M5 Forecasting datasets shows superior model performance (e.g., 0.874 Accuracy, 0.685 RMSSE) alongside significant MLOps efficiency gains. [3]
• Population Stability Index (PSI): A metric used to quantify data drift by comparing the distribution of observations in bins between a reference and current dataset. [3]
• The LLM-integrated IDE transforms traditional development by embedding intelligence throughout the ML lifecycle, providing code generation, debugging recommendations, and automatic pipeline configuration. [2]
• The backend incorporates automated data validation using KL divergence, a centralized Feature Store, and CI/CD orchestration via Docker and Kubernetes, ensuring robust and consistent ML operations. [2]
• A continuous monitoring and retraining engine utilizes Population Stability Index (PSI) and a Bayesian updating policy to automatically trigger retraining pipelines when model drift is detected, maintaining optimal performance in production. [2]
• The framework democratizes MLOps by automating tasks that traditionally require specialized DevOps expertise, making advanced ML lifecycle management accessible to a broader range of data scientists and ML engineers. [2]
• LLM-Integrated IDE: An Integrated Development Environment that embeds a Large Language Model assistant for intelligent code assistance and automated MLOps pipeline configuration. [2]
• Automated MLOps Pipelines: Backend services that automate the machine learning lifecycle, including data validation, feature storage, model versioning, CI/CD orchestration, and continuous monitoring. [2]
• SmartMLOps Studio significantly reduces ML pipeline configuration time by 61% by integrating an LLM assistant for automated pipeline generation, streamlining operational complexities. [1]

Why we think this paper is great for you:
This research provides insights into how job search behaviors evolve during unemployment, offering valuable data on dynamics within the labor market. It directly relates to understanding the experiences of individuals facing employment transitions.

Abstract
We study how job search behavior evolves over the unemployment spell and the extent to which job seekers experience duration dependence in callbacks. Leveraging data on 2.4 million monthly activity reports containing detailed information on job applications, interviews, and other search activities, we separate within-spell changes from dynamic selection with a time-and-spell fixed effects design. We find that raw search effort increases with unemployment duration, but this pattern reflects dynamic selection: within-spell search effort remains flat and declines sharply in the months preceding re-employment. Around unemployment insurance (UI) exhaustion, search effort drops by approximately 10%, likely due to participation in labor market programs crowding out job search. Reported interviews indicate that callbacks decline by 6% per month, but only 10--14% of this decline reflects ``true'' duration dependence. Finally, we document substantial heterogeneity: search effort and duration dependence vary strongly by age, and job seekers in tight labor markets experience about 50% more duration dependence.

Why we think this paper is great for you:
This study offers a detailed analysis of wage dynamics and disparities within the labor market, which is highly relevant to your interest in understanding broader labor market changes. It provides empirical evidence on a significant aspect of employment.

Abstract
This study analyzes the gender gap in desired wages using large administrative data of public job referrals, which allows us to look at the desired salaries of individuals from a wider wage distribution. We conduct a decomposition analysis using available information on age, desired work region, and desired occupation. We find that of the three factors, desired occupation is the most important in generating differences in desired wages; however, the residuals are the largest outside of the three factors. To further probe the unexplained residuals, we also conduct heterogeneity and sensitivity analyses using the available data.

Why we think this paper is great for you:
This paper explores the practical application of autonomous systems in a complex environment like agriculture, aligning with your interest in real-world advanced AI applications. It details the challenges and frameworks for deploying advanced automation.

Abstract
The agricultural sector increasingly relies on autonomous systems that operate in complex and variable environments. Unlike on-road applications, agricultural automation integrates driving and working processes, each of which imposes distinct operational constraints. Handling this complexity and ensuring consistency throughout the development and validation processes requires a structured, transparent, and verified description of the environment. However, existing Operational Design Domain (ODD) concepts do not yet address the unique challenges of agricultural applications. Therefore, this work introduces the Agricultural ODD (Ag-ODD) Framework, which can be used to describe and verify the operational boundaries of autonomous agricultural systems. The Ag-ODD Framework consists of three core elements. First, the Ag-ODD description concept, which provides a structured method for unambiguously defining environmental and operational parameters using concepts from ASAM Open ODD and CityGML. Second, the 7-Layer Model derived from the PEGASUS 6-Layer Model, has been extended to include a process layer to capture dynamic agricultural operations. Third, the iterative verification process verifies the Ag-ODD against its corresponding logical scenarios, derived from the 7-Layer Model, to ensure the Ag-ODD's completeness and consistency. Together, these elements provide a consistent approach for creating unambiguous and verifiable Ag-ODD. Demonstrative use cases show how the Ag-ODD Framework can support the standardization and scalability of environmental descriptions for autonomous agricultural systems.

Why we think this paper is great for you:
While the title mentions "job completion," this paper focuses on optimizing computational task scheduling rather than human labor market dynamics. It might offer insights into system efficiency, but it's less directly aligned with your primary interests.

Abstract
We consider a time-slotted job-assignment system with a central server, N users and a machine which changes its state according to a Markov chain (hence called a Markov machine). The users submit their jobs to the central server according to a stochastic job arrival process. For each user, the server has a dedicated job queue. Upon receiving a job from a user, the server stores that job in the corresponding queue. When the machine is not working on a job assigned by the server, the machine can be either in internally busy or in free state, and the dynamics of these states follow a binary symmetric Markov chain. Upon sampling the state information of the machine, if the server identifies that the machine is in the free state, it schedules a user and submits a job to the machine from the job queue of the scheduled user. To maximize the number of jobs completed per unit time, we introduce a new metric, referred to as the age of job completion. To minimize the age of job completion and the sampling cost, we propose two policies and numerically evaluate their performance. For both of these policies, we find sufficient conditions under which the job queues will remain stable.