Tootfinder

Human-AI Schema Discovery and Application for Creative Problem Solving
Sitong Wang
https://arxiv.org/abs/2508.05045 https://arxiv.org/pdf/2508.05045…

Human-AI Schema Discovery and Application for Creative Problem Solving
Humans often rely on underlying structural patterns-schemas-to create, whether by writing stories, designing software, or composing music. Schemas help organize ideas and guide exploration, but they are often difficult to discover and apply, especially in complex or unfamiliar domains. My Ph.D. research develops a framework for human-AI schema discovery and application to support creative problem solving. I design systems that support users in sensemaking over examples to abstract schemas, and …

XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL
Yifu Liu, Yin Zhu, Yingqi Gao, Zhiling Luo, Xiaoxia Li, Xiaorong Shi, Yuntao Hong, Jinyang Gao, Yu Li, Bolin Ding, Jingren Zhou
https://arxiv.org/abs/2507.04701

XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL
To leverage the advantages of LLM in addressing challenges in the Text-to-SQL task, we present XiYan-SQL, an innovative framework effectively generating and utilizing multiple SQL candidates. It consists of three components: 1) a Schema Filter module filtering and obtaining multiple relevant schemas; 2) a multi-generator ensemble approach generating multiple highquality and diverse SQL queries; 3) a selection model with a candidate reorganization strategy implemented to obtain the optimal SQL q…

AI-assisted JSON Schema Creation and Mapping
Felix Neubauer, J\"urgen Pleiss, Benjamin Uekermann
https://arxiv.org/abs/2508.05192 https://arxiv.org/pd…

AI-assisted JSON Schema Creation and Mapping
Model-Driven Engineering (MDE) places models at the core of system and data engineering processes. In the context of research data, these models are typically expressed as schemas that define the structure and semantics of datasets. However, many domains still lack standardized models, and creating them remains a significant barrier, especially for non-experts. We present a hybrid approach that combines large language models (LLMs) with deterministic techniques to enable JSON Schema creation, m…

This https://arxiv.org/abs/2506.01056 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

MCP-Zero: Proactive Toolchain Construction for LLM Agents from Scratch
Function-calling has enabled large language models (LLMs) to act as tool-using agents, but injecting thousands of tool schemas into the prompt is costly and error-prone. We introduce MCP-Zero, a proactive agent framework that lets the LLM itself decide when and which external tools to retrieve, thereby assembling a task-specific toolchain from scratch. The framework is built upon three components: (1) Proactive Tool Request, where the model emits a structured $\left<\operatorname{tool\_assistan…

Retrieval-Augmented Generation of Ontologies from Relational Databases
Mojtaba Nayyeri, Athish A Yogi, Nadeen Fathallah, Ratan Bahadur Thapa, Hans-Michael Tautenhahn, Anton Schnurpel, Steffen Staab
https://arxiv.org/abs/2506.01232

Retrieval-Augmented Generation of Ontologies from Relational Databases
Transforming relational databases into knowledge graphs with enriched ontologies enhances semantic interoperability and unlocks advanced graph-based learning and reasoning over data. However, previous approaches either demand significant manual effort to derive an ontology from a database schema or produce only a basic ontology. We present RIGOR, Retrieval-augmented Iterative Generation of RDB Ontologies, an LLM-driven approach that turns relational schemas into rich OWL ontologies with minimal…

Characterizing Small Circuit Classes from FAC^0 to FAC^1 via Discrete Ordinary Differential Equations
Melissa Antonelli, Arnaud Durand, Juha Kontinen
https://arxiv.org/abs/2506.23404

Characterizing Small Circuit Classes from FAC^0 to FAC^1 via Discrete Ordinary Differential Equations
In this paper, we provide a uniform framework for investigating small circuit classes and bounds through the lens of ordinary differential equations (ODEs). Following an approach recently introduced to capture the class of polynomial-time computable functions via ODE-based recursion schemas and later applied to the context of functions computed by unbounded fan-in circuits of constant depth (FAC^0), we study multiple relevant small circuit classes. In particular, we show that natural restrictio…

✍️ Handles challenging edge cases like handwritten text in multiple languages with remarkable accuracy where conventional systems completely fail
🎯 Advanced structured data extraction using custom schemas for specific information like financial figures, medical form data and checkbox interpretations
📋 Outputs clean #markdown format with comprehensive

Enhancing Manufacturing Knowledge Access with LLMs and Context-aware Prompting
Sebastian Monka, Irlan Grangel-Gonz\'alez, Stefan Schmid, Lavdim Halilaj, Marc Rickart, Oliver Rudolph, Rui Dias
https://arxiv.org/abs/2507.22619

Enhancing Manufacturing Knowledge Access with LLMs and Context-aware Prompting
Knowledge graphs (KGs) have transformed data management within the manufacturing industry, offering effective means for integrating disparate data sources through shared and structured conceptual schemas. However, harnessing the power of KGs can be daunting for non-experts, as it often requires formulating complex SPARQL queries to retrieve specific information. With the advent of Large Language Models (LLMs), there is a growing potential to automatically translate natural language queries into…

BDIViz: An Interactive Visualization System for Biomedical Schema Matching with LLM-Powered Validation
Eden Wu, Dishita G Turakhia, Guande Wu, Christos Koutras, Sarah Keegan, Wenke Liu, Beata Szeitz, David Fenyo, Cl\'audio T. Silva, Juliana Freire
https://arxiv.org/abs/2507.16117

BDIViz: An Interactive Visualization System for Biomedical Schema Matching with LLM-Powered Validation
Biomedical data harmonization is essential for enabling exploratory analyses and meta-studies, but the process of schema matching - identifying semantic correspondences between elements of disparate datasets (schemas) - remains a labor-intensive and error-prone task. Even state-of-the-art automated methods often yield low accuracy when applied to biomedical schemas due to the large number of attributes and nuanced semantic differences between them. We present BDIViz, a novel visual analytics sy…

Automated Test Data Generation for Enterprise Protobuf Systems: A Metaclass-Enhanced Statistical Approach
Y. Du
https://arxiv.org/abs/2507.22070 https://ar…

Automated Test Data Generation for Enterprise Protobuf Systems: A Metaclass-Enhanced Statistical Approach
Large-scale enterprise systems utilizing Protocol Buffers (protobuf) present significant challenges for performance testing, particularly when targeting intermediate business interfaces with complex nested data structures. Traditional test data generation approaches are inadequate for handling the intricate hierarchical and graph-like structures inherent in enterprise protobuf schemas. This paper presents a novel test data generation framework that leverages Python's metaclass system for dynami…

Setting The Table with Intent: Intent-aware Schema Generation and Editing for Literature Review Tables
Vishakh Padmakumar, Joseph Chee Chang, Kyle Lo, Doug Downey, Aakanksha Naik
https://arxiv.org/abs/2507.19521

Setting The Table with Intent: Intent-aware Schema Generation and Editing for Literature Review Tables
The increasing volume of academic literature makes it essential for researchers to organize, compare, and contrast collections of documents. Large language models (LLMs) can support this process by generating schemas defining shared aspects along which to compare papers. However, progress on schema generation has been slow due to: (i) ambiguity in reference-based evaluations, and (ii) lack of editing/refinement methods. Our work is the first to address both issues. First, we present an approach…

Is SHACL Suitable for Data Quality Assessment?
Carolina Cort\'es Lasalle, Lisa Ehrlinger, Lorena Etcheverry, Felix Naumann
https://arxiv.org/abs/2507.22305 https://

Is SHACL Suitable for Data Quality Assessment?
Knowledge graphs have been widely adopted in both enterprises, such as the Google Knowledge Graph, and open platforms like Wikidata to represent domain knowledge and support analysis with artificial intelligence. They model real-world information as nodes and edges. To embrace flexibility, knowledge graphs often lack enforced schemas (i.e., ontologies), leading to potential data quality issues, such as semantically overlapping nodes. Therefore, ensuring their quality is essential, as issues in …

Day 0:
TL;DR: Set up the basic foundations for a new system: database schema finalized, core backend modules scaffolded, and initial API contracts defined. The real work begins now.
No hype. Just building.
Today I worked on building the core backend infrastructure for a cloud-native insurance platform. Key points:
Modular microservices architecture based on NestJS
PostgreSQL schemas designed for flexibility — no hardcoded values, all dynamic
Using TypeScript w…

Making REST APIs Agent-Ready: From OpenAPI to Model Context Protocol Servers for Tool-Augmented LLMs
Meriem Mastouri, Emna Ksontini, Wael Kessentini
https://arxiv.org/abs/2507.16044

Making REST APIs Agent-Ready: From OpenAPI to Model Context Protocol Servers for Tool-Augmented LLMs
Large Language Models (LLMs) are evolving from passive text generators into active agents that invoke external tools. To support this shift, scalable protocols for tool integration are essential. The Model Context Protocol (MCP), introduced by Anthropic in 2024, offers a schema-driven standard for dynamic tool discovery and invocation. Yet, building MCP servers remains manual and repetitive, requiring developers to write glue code, handle authentication, and configure schemas by hand-replicatin…

Replaced article(s) found for cs.DB. https://arxiv.org/list/cs.DB/new
[1/1]:
- Towards the Automated Extraction and Refactoring of NoSQL Schemas from Application Code
Carlos J. Fernandez-Candel, Anthony Cleve, Jesus J. Garc\'ia-Molina