Tootfinder

✍️ 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

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…

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…

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…