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Snowflake AI Research
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AI-native SQL queries expose critical gaps in model performance, with top proprietary models still struggling to achieve 70% execution accuracy.
Training LLMs without ground-truth solutions can yield significant performance improvements, as shown by RiVER's success in enhancing both score-based and exact-solution benchmarks.
Efficiently navigating hierarchical memory can cut token usage by over 77% while boosting task performance in LLMs.
Text-to-SQL models can now achieve significantly higher accuracy by grouping and ranking SQL candidates based on execution results, then strategically resampling when the initial pool is lacking.
Stop hand-crafting hints for RL agents: HiLL learns to generate adaptive hints that actually improve the agent's performance on the original task, not just the hinted one.
Forget prompt engineering – LSE trains LLMs to self-edit their own contexts at test time, outperforming even GPT-5 and Claude Sonnet 4.5 in Text-to-SQL and question answering.
