Data contracts formalize agreements between data producers and consumers regarding schema, semantics, and quality expectations. As data pipelines grow in complexity, manual authoring and maintenance of contracts becomes error-prone and labor-intensive. We present an AI-driven framework for automatic data contract generation using large language models (LLMs). Our system leverages parameter-efficient fine-tuning methods, including LoRA and PEFT, to adapt LLMs to structured data domains. The models take sample data or schema descriptions and output validated contract definitions in formats such as JSON Schema and Avro. We integrate this framework into modern data platforms (e.g., Databricks, Snowflake) to automate contract enforcement at scale. Experimental results on synthetic and real-world datasets demonstrate that the fine-tuned LLMs achieve high accuracy in generating valid contracts and reduce manual workload by over 70%. We also discuss key challenges such as hallucination, version control, and the need for continuous learning. This work demonstrates that generative AI can enable scalable, agile data governance by bridging the gap between intent and implementation in enterprise data management.

Retrieval practice is a well-established pedagogical technique known to significantly enhance student learning and knowledge retention. However, generating high-quality retrieval practice questions is often time-consuming and labor intensive for instructors, especially in rapidly evolving technical subjects. Large Language Models (LLMs) offer the potential to automate this process by generating questions in response to prompts, yet the effectiveness of LLM-generated retrieval practice on student learning remains to be established. In this study, we conducted an empirical study involving two college-level data science courses, with approximately 60 students. We compared learning outcomes during one week in which students received LLM-generated multiple-choice retrieval practice questions to those from a week in which no such questions were provided. Results indicate that students exposed to LLM-generated retrieval practice achieved significantly higher knowledge retention, with an average accuracy of 89%, compared to 73% in the week without such practice. These findings suggest that LLM-generated retrieval questions can effectively support student learning and may provide a scalable solution for integrating retrieval practice into real-time teaching. However, despite these encouraging outcomes and the potential time-saving benefits, cautions must be taken, as the quality of LLM-generated questions can vary. Instructors must still manually verify and revise the generated questions before releasing them to students. K eywords Retrieval practice large language models generative AI student learning multiple-choice questions STEM education data science higher education 1 Introduction Retrieval practice [30], frequently termed the "testing effect" [3], is a teaching technique that has been extensively studied. Empirical evidence has clearly indicated its ability to enhance long-term memory retention and overall learning [29, 3, 24, 27, 18, 2]. This technique involves the active recall of information from memory, a process that inherently strengthens neural connections and improves the future retrieval of that information.

--The increasing sophistication and integration of Generative AI (GenAI) models into diverse applications introduce new security challenges that traditional methods struggle to address. This research explores the critical need for proactive security measures to mitigate the risks associated with malicious exploitation of GenAI systems. We present a framework encompassing key approaches, tools, and strategies designed to outmaneuver even advanced adversarial attacks, emphasizing the importance of securing GenAI innovation against potential liabilities. We also empirically prove the effectiveness of the said framework by testing it against the SPML Chatbot Prompt Injection Dataset. This work highlights the shift from reactive to proactive security practices essential for the safe and responsible deployment of GenAI technologies.

As large language models demonstrate enormous potential in the field of Electronic Design Automation (EDA), generative AI-assisted chip design is attracting widespread attention from academia and industry. Although these technologies have made preliminary progress in tasks such as code generation, their application in chip verification -- a critical bottleneck in the chip development cycle -- remains at an exploratory stage. This paper proposes an innovative Multi-Agent Verification Framework (MAVF) aimed at addressing the limitations of current single-LLM approaches in complex verification tasks. Our framework builds an automated transformation system from design specifications to testbench through the collaborative work of multiple specialized agents, including specification parsing, verification strategy generation, and code implementation. Through verification experiments on multiple chip modules of varying complexity, results show that MAVF significantly outperforms traditional manual methods and single-dialogue generative AI approaches in verification document parsing and generation, as well as automated testbench generation. This research opens new directions for exploring generative AI applications in verification automation, potentially providing effective approaches to solving the most challenging bottleneck issues in chip design.

A handful of college students who were part of OpenAI's testing cohort--hailing from Princeton, Wharton, and the University of Minnesota--shared positive reviews of Study Mode, saying it did a good job of checking their understanding and adapting to their pace. The learning approaches that OpenAI has programmed into Study Mode, which are based partially on Socratic methods, appear sound, says Christopher Harris, an educator in New York who has created a curriculum aimed at AI literacy. They might grant educators more confidence about allowing, or even encouraging, their students to use AI. "Professors will see this as working with them in support of learning as opposed to just being a way for students to cheat on assignments," he says. As demonstrated in OpenAI's recent partnership with leading teachers' unions, the company is currently trying to rebrand chatbots as tools for personalized learning rather than cheating.

The school year starts soon for many students, and ChatGPT has announced a new "study mode" that aims to prevent--or at least, encourage against--students taking homework shortcuts. The mode is designed around the Socratic method, so when activated, OpenAI's generative AI chatbot rejects direct requests for answers, instead guiding the user with open-ended questions. The new study mode is available to most logged-in users of ChatGPT, including those on the free version. OpenAI has significantly disrupted the education system over the past few years, with students becoming some of the earliest adopters of ChatGPT. Even so, OpenAI claims the bot is currently an overall boon to learners--if asked to roleplay as a synthetic tutor.

Mark Zuckerberg is on a warpath to recruit top talent in the AI field for his newly formed Meta Superintelligence Labs. After trying to gut OpenAI (and successfully poaching several top researchers), he appears to have set his sights on his next target. More than a dozen people at Mira Murati's 50-person startup, Thinking Machines Lab, have been approached or received offers from the tech giant. One of those offers was more than 1 billion over a multi-year span, a source with knowledge of the negotiations tells WIRED. The rest were between 200 million and 500 million over a four-year span, multiple sources confirm.

The investigation of allele frequency trajectories in populations evolving under controlled environmental pressures has become a popular approach to study evolutionary processes on the molecular level. Statistical models based on well-defined evolutionary concepts can be used to validate different hypotheses about empirical observations. Despite their popularity, classic statistical models like the Wright-Fisher model suffer from simplified assumptions such as the independence of selected loci along a chromosome and uncertainty about the parameters. Deep generative neural networks offer a powerful alternative known for the integration of multivariate dependencies and noise reduction. Due to their high data demands and challenging interpretability they have, so far, not been widely considered in the area of population genomics. To address the challenges in the area of Evolve and Resequencing experiments (E&R) based on pooled sequencing (Pool-Seq) data, we introduce a deep generative neural network that aims to model a concept of evolution based on empirical observations over time. The proposed model estimates the distribution of allele frequency trajectories by embedding the observations from single nucleotide polymorphisms (SNPs) with information from neighboring loci. Evaluation on simulated E&R experiments demonstrates the model's ability to capture the distribution of allele frequency trajectories and illustrates the representational power of deep generative models on the example of linkage disequilibrium (LD) estimation. Inspecting the internally learned representations enables estimating pairwise LD, which is typically inaccessible in Pool-Seq data. Our model provides competitive LD estimation in Pool-Seq data high degree of LD when compared to existing methods.

With the rapid advancement of generative models, associated privacy concerns have attracted growing attention. To address this, researchers have begun adapting machine unlearning techniques from traditional classification models to generative settings. Although notable progress has been made in this area, a unified framework for systematically organizing and integrating existing work is still lacking. The substantial differences among current studies in terms of unlearning objectives and evaluation protocols hinder the objective and fair comparison of various approaches. While some studies focus on specific types of generative models, they often overlook the commonalities and systematic characteristics inherent in Generative Model Unlearning (GenMU). To bridge this gap, we provide a comprehensive review of current research on GenMU and propose a unified analytical framework for categorizing unlearning objectives, methodological strategies, and evaluation metrics. In addition, we explore the connections between GenMU and related techniques, including model editing, reinforcement learning from human feedback, and controllable generation. We further highlight the potential practical value of unlearning techniques in real-world applications. Finally, we identify key challenges and outline future research directions aimed at laying a solid foundation for further advancements in this field. We consistently maintain the related open-source materials at https://github.com/caxLee/Generative-model-unlearning-survey.

As Large Language Models (LLMs) increasingly power autonomous agents in robotics and embodied AI, understanding their spatial reasoning capabilities becomes crucial for ensuring reliable real-world deployment. Despite advances in language understanding, current research lacks evaluation of how LLMs perform spatial navigation without visual cues, a fundamental requirement for agents operating with limited sensory information. This paper addresses this gap by introducing MazeEval, a benchmark designed to isolate and evaluate pure spatial reasoning in LLMs through coordinate-based maze navigation tasks. Our methodology employs a function-calling interface where models navigate mazes of varying complexity ($5\times 5$ to $15\times 15$ grids) using only coordinate feedback and distance-to-wall information, excluding visual input to test fundamental spatial cognition. We evaluate eight state-of-the-art LLMs across identical mazes in both English and Icelandic to assess cross-linguistic transfer of spatial abilities. Our findings reveal striking disparities: while OpenAI's O3 achieves perfect navigation for mazes up to size $30\times 30$, other models exhibit catastrophic failure beyond $9\times 9$ mazes, with 100% of failures attributed to excessive looping behavior where models revisit a cell at least 10 times. We document a significant performance degradation in Icelandic, with models solving mazes 3-4 sizes smaller than in English, suggesting spatial reasoning in LLMs emerges from linguistic patterns rather than language-agnostic mechanisms. These results have important implications for global deployment of LLM-powered autonomous systems, showing spatial intelligence remains fundamentally constrained by training data availability and highlighting the need for architectural innovations to achieve reliable navigation across linguistic contexts.