CrypFormBench is a new benchmark jointly covering symbolic and computational security to evaluate LLMs on five formal analysis capabilities, with results showing top model Claude-3.5 scores 48.7/100 and most models struggling on generation, transformation, and correction.
Towards Complex Text-to- SQL in Cross-Domain Database with Intermediate Representation
5 Pith papers cite this work. Polarity classification is still indexing.
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2026 5representative citing papers
CAPER derives clause-aligned supervision via SQL AST counterfactuals to train a Clause-PRM that improves execution accuracy up to 15.3% relative and failure localization to 84.53% accuracy on BIRD and Spider.
NL2SQLBench is a new modular benchmarking framework that evaluates LLM NL2SQL methods across three core modules on existing datasets, exposing large accuracy gaps and computational inefficiency.
FlexSQL reaches 65.4% on Spider2-Snow by allowing agents to flexibly explore schemas, generate diverse plans, choose SQL or Python execution, and apply two-tiered repair.
SPENCE shows older NL2SQL benchmarks like Spider have high performance sensitivity to syntactic changes, indicating likely training contamination, while newer ones like BIRD show little sensitivity and appear largely clean.
citing papers explorer
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CrypFormBench: Benchmarking Formal Analysis Capability of Large Language Models for Cryptographic Schemes
CrypFormBench is a new benchmark jointly covering symbolic and computational security to evaluate LLMs on five formal analysis capabilities, with results showing top model Claude-3.5 scores 48.7/100 and most models struggling on generation, transformation, and correction.
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CAPER: Clause-Aligned Process Supervision for Text-to-SQL
CAPER derives clause-aligned supervision via SQL AST counterfactuals to train a Clause-PRM that improves execution accuracy up to 15.3% relative and failure localization to 84.53% accuracy on BIRD and Spider.
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NL2SQLBench: A Modular Benchmarking Framework for LLM-Enabled NL2SQL Solutions
NL2SQLBench is a new modular benchmarking framework that evaluates LLM NL2SQL methods across three core modules on existing datasets, exposing large accuracy gaps and computational inefficiency.
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FlexSQL: Flexible Exploration and Execution Make Better Text-to-SQL Agents
FlexSQL reaches 65.4% on Spider2-Snow by allowing agents to flexibly explore schemas, generate diverse plans, choose SQL or Python execution, and apply two-tiered repair.
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SPENCE: A Syntactic Probe for Detecting Contamination in NL2SQL Benchmarks
SPENCE shows older NL2SQL benchmarks like Spider have high performance sensitivity to syntactic changes, indicating likely training contamination, while newer ones like BIRD show little sensitivity and appear largely clean.