Abstract The integration of Generative Artificial Intelligence (GenAI) with modern database systems is transforming how we store, query, and interpret multimodal data. This paper presents an empirical investigation into GenAI enabled semantic music search, combining generative audio embeddings with vector database technologies to support instrument specific retrieval. Using a controlled dataset of aligned multitrack recordings including saxophone, piano, percussion, and mixed audio we evaluate the... The integration of Generative Artificial Intelligence (GenAI) with modern database systems is transforming how we store, query, and interpret multimodal data. This paper presents an empirical investigation into GenAI enabled semantic music search, combining generative audio embeddings with vector database technologies to support instrument specific retrieval. Using a controlled dataset of aligned multitrack recordings including saxophone, piano, percussion, and mixed audio we evaluate the performance of generative embedding models (e.g., AudioMAE) against traditional signal based features such as MFCCs and spectral centroids. Results demonstrate that generative embeddings significantly outperform classical methods, achieving a Recall@1 of 0.67 compared to 0.44 for MFCCs, and exhibit strong cross instrument generalization, with top-1 retrieval accuracy exceeding 84% across all instrument categories. We further assess vector indexing strategies Flat, IVF, and HNSW and find that HNSW offers the best trade off between latency (2.1 ms), memory efficiency (32 MB), and recall (0.95), making it ideal for real time applications. The proposed architecture is not only effective for saxophone centric queries but also readily extensible to other instruments, including the violin, as evidenced by its compatibility with datasets such as TRIOS. Beyond music retrieval, our findings reflect broader trends in GenAI database integration, including probabilistic querying (e.g., GenSQL), distributed cloud scalability, and educational implications. We conclude that the fusion of generative representation learning and purpose built vector databases constitutes a scalable, accurate, and deployable framework suitable for both research prototypes and industrial systems in creative, educational, and enter prise contexts. This work underscores the need to co design AI models and database infrastructure to unlock intelligent, uncertainty aware, and user accessible data ecosystems. Read More Read Less

Abstract This paper presents an empirical study on the impact of LLM-assisted metadata enrichment on the reliability and performance of enterprise grade Generative AI (GenAI) systems, using the OECD IFCMA Climate Policy Dashboard as a real world testbed. The dataset comprising over 1,600 heterogeneous climate policy instruments across 43 approaches and multiple countries exhibits significant semantic inconsistency and incomplete metadata, reflecting common challenges in multinational... This paper presents an empirical study on the impact of LLM-assisted metadata enrichment on the reliability and performance of enterprise grade Generative AI (GenAI) systems, using the OECD IFCMA Climate Policy Dashboard as a real world testbed. The dataset comprising over 1,600 heterogeneous climate policy instruments across 43 approaches and multiple countries exhibits significant semantic inconsistency and incomplete metadata, reflecting common challenges in multinational data environments. The authors implement a three stage GenAI enabled pipeline: (1) definition of a structured metadata schema, (2) LLM-driven semantic enrichment to infer missing fields and harmonize terminology, and (3) a metadata aware Retrieval Augmented Generation (RAG) system that leverages enriched context for grounded responses. Quantitative evaluation demonstrates a statistically significant improvement in metadata completeness from a mean of 0.41 to 0.83 (p < 0.001) and a marked increase in cross country semantic consistency, with cosine similarity rising from 0.64 to 0.85 for carbon pricing policies. These enhancements directly translate into tangible RAG performance gains: retrieval precision improves by 25.8%, answer faithfulness by 23.5%, and hallucination rates decline by 41.9%. Crucially, correlation analysis confirms a strong positive relationship between metadata quality and GenAI reliability ( > 0.7). The study positions high quality metadata not as auxiliary documentation but as a foundational architectural component that enables observability, explainability, and trust in high stakes GenAI applications. By bridging a critical gap between conceptual frameworks and empirical validation, this work establishes metadata centric design as essential for scalable, governance aligned, and reliable enterprise AI systems. Read More Read Less

Abstract This paper explores the intersection of two critical frontiers in data science: the rise of foundation models for tabular data and their practical application to forecasting in complex, real world domains specifically global commodity markets. The study leverages the World Bank's Pink Sheet dataset (1960-2025) to analyze long term price dynamics and evaluate forecasting methodologies across energy, metal, and agricultural commodities. Empirical results show... This paper explores the intersection of two critical frontiers in data science: the rise of foundation models for tabular data and their practical application to forecasting in complex, real world domains specifically global commodity markets. The study leverages the World Bank's Pink Sheet dataset (1960-2025) to analyze long term price dynamics and evaluate forecasting methodologies across energy, metal, and agricultural commodities. Empirical results show that traditional time series models like SARIMAX excel in stable, seasonal contexts, while machine learning approaches particularly Random Forest better capture nonlinearities and structural breaks during periods of high volatility, such as those observed during the 2020 pandemic shock. In parallel, the paper reviews recent breakthroughs in tabular foundation models, including TabPFN, TabICL, and TabFM, which challenge the longstanding dominance of gradient boosted trees by enabling in context learning, zero shot generalization, and instruction following capabilities. These models leverage synthetic pretraining and novel architectures to achieve competitive or superior performance on small to medium datasets. Despite these advances, the potential of foundation models for tabular data distillation compressing large datasets while preserving statistical fidelity remains underexplored. The authors argue that extending foundation models beyond prediction to tasks like dataset compression, synthetic generation, and privacypreserving representation could address key challenges in web science, including scalability, reproducibility, and data sharing. The paper concludes by advocating a dual research trajectory: advancing neural architectures tailored to tabular structure and broadening their scope from predictive modeling to data centric infrastructure. This integrated vision promises more efficient, generalizable, and ethically robust tools for managing the growing complexity of real world tabular data. Read More Read Less