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Cross-AI ReNL2SQL 2026 : 1st International Workshop on Reliable NL2SQL for Enterprise AI Systems | |||||||||||||
| Link: https://cross-ai.io/conference/2027/preconf/nl2sql-2026/ | |||||||||||||
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Call For Papers | |||||||||||||
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Cross-AI Workshop on Reliable NL2SQL for Enterprise AI Systems (ReNL2SQL 2026) will be held virtually on Dec. 14-16, 2026 in the Cross-AI Symposium 2026 (https://cross-ai.io/conference/2027/preconf/).
Authors are encouraged to select their preferred venue when submitting their papers/posters/demos. Please visit workshop webpage for more details and submission instructions. ________________________________________ Introduction Natural Language to SQL (NL2SQL) has advanced rapidly with Large Language Models (LLMs), achieving strong performance on benchmarks such as Spider and BIRD. However, these gains have not translated into reliable deployment in real-world enterprise and high-stakes application domains, including finance, healthcare, logistics, and enterprise operations. A key challenge is a persistent gap between benchmark assumptions and production reality. Existing benchmarks typically assume small, clean, and static schemas; fully specified user intent; limited join complexity; few-database settings; and accuracy-centric evaluation. These simplifications support rapid progress on benchmark metrics but fail to reflect real-world conditions. In practice, enterprise and domain-specific systems are significantly more complex. They span thousands of evolving, poorly documented tables across distributed cloud platforms, with inconsistent business definitions, strict access controls, and heterogeneous data types. This makes both structure and semantics difficult to navigate, requiring user queries to be translated from high-level intent into grounded schemas, disambiguated metrics, and cost-aware execution plans across multiple systems. Despite strong benchmark performance, real-world deployment often fails due to incorrect joins, hallucinated tables or columns, weak grounding in domain metadata, limited scalability, and a lack of cost and system awareness that can lead to unsafe or overly expensive queries. Meanwhile, NL2SQL is evolving from single-step query generation into agentic analytics systems that must retrieve and reason over schemas, plan multi-step workflows, interact through clarification, execute and validate SQL, and repair failures while operating under cost, latency, and governance constraints. This workshop frames NL2SQL as a real-world AI systems challenge at the intersection of foundation models, databases, cloud infrastructure, and agentic reasoning. It positions NL2SQL as a core component of enterprise and high-stakes AI infrastructure and aims to shape a research agenda focused on trustworthy, scalable, cross-aware, and production-ready NL2SQL systems, bridging the gap between benchmark success and real-world impact. ________________________________________ Core Tracks 1. Enterprise NL2SQL and Foundation Models 2. Agentic and Conversational Data Systems 3. Cross-Systems and Cloud-Scale Analytics 4. Safety, Reliability, and Governance 5. Advanced Reasoning and Applications ________________________________________ Topics & Important Dates (AoE) Please visit the workshop website: https://www.cross-ai.io/conference/2027/preconf/nl2sql-2026/ ________________________________________ Submission Please follow the workshop website to submit papers/posters/demos. Accepted full/short/poster/demo papers will be published in the indexed proceedings. ________________________________________ Cross-AI Google Group Welcome to subscribe to the Cross-AI Google group (https://groups.google.com/g/multimodal-ai). |
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