Aditya Gudal | Searcharxiv

arXiv2026

CSR-RAG: An Efficient Retrieval System for Text-to-SQL on the Enterprise Scale

Natural language to SQL translation (Text-to-SQL) is one of the long-standing problems that has recently benefited from advances in Large Language Models (LLMs). While most academic Text-to-SQL benchmarks request schema description as a part of natural language input, enterprise-scale applications often require table retrieval before SQL query generation. To address this need, we propose a novel hybrid Retrieval Augmented Generation (RAG) system consisting of contextual, structural, and relational retrieval (CSR-RAG) to achieve computationally efficient yet sufficiently accurate retrieval for enterprise-scale databases. Through extensive enterprise benchmarks, we demonstrate that CSR-RAG achieves up to 40% precision and over 80% recall while incurring a negligible average query generation latency of only 30ms on commodity data center hardware, which makes it appropriate for modern LLM-based enterprise-scale systems.

cs.CL↗

arXiv2025

A Ranking Framework for Network Resource Allocation and Scheduling via Hypergraphs

Resource allocation and scheduling are a common problem in various distributed systems. Although widely studied, the state-of-the-art solutions either do not scale or lack the expressive power to capture the most complex instances of the problem. To that end, we present a mathematical framework for hypergraph ranking and analysis, unifying graph theory, lattice theory, and semantic analysis. In our fundamental theorem, we prove the existence of partial order on entities of hypergraphs, extending traditional hypergraph analysis by introducing semantic operators that capture relationships between vertices and hyperedges. Within the boundaries of our framework, we introduce an algorithm to rank the node-hyperedge pairs with respect to the captured semantics. The strength of our approach lies in its applicability to complex ranking problems that can be modeled as hypergraphs, including network resource allocation, task scheduling, and table selection in Text-to-SQL. Through simulations, we demonstrate that our framework delivers nearly optimal problem solutions at a superior run time performance.

cs.DS↗