DeepEye-SQL applies SDLC-inspired orchestration to Text-to-SQL, achieving 73.5% on BIRD-Dev, 75.07% on BIRD-Test, and 89.8% on Spider-Test with ~30B MoE models.
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5 Pith papers cite this work, alongside 262 external citations. Polarity classification is still indexing.
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A single-pass oracle-generation method built from folded static context and LLM-selected runtime states outperforms iterative self-repair on mutation-based fault detection.
Adding project-dependency awareness and C++-specific knowledge to LLM prompts raises average compilation success from roughly 32% to 83% and more than doubles line and branch coverage across ten C++ projects.
AEGIS combines concise context extraction with finite-state-machine feedback control to make LLM agents reproduce more software bugs from issue descriptions than existing baselines.
Generative AI suitability in qualitative research depends primarily on the approach (small-q positivist/post-positivist or Big Q non-positivist) along with skills, ethics, and personal preferences.
citing papers explorer
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DeepEye-SQL: A Software-Engineering-Inspired Text-to-SQL Framework
DeepEye-SQL applies SDLC-inspired orchestration to Text-to-SQL, achieving 73.5% on BIRD-Dev, 75.07% on BIRD-Test, and 89.8% on Spider-Test with ~30B MoE models.
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To Vibe Research or Not to Vibe Research? Generative AI in Qualitative Research
Generative AI suitability in qualitative research depends primarily on the approach (small-q positivist/post-positivist or Big Q non-positivist) along with skills, ethics, and personal preferences.