UniQL is a human-verified benchmark providing aligned natural language questions and dialect-specific SQL queries for 16 SQL systems to evaluate cross-dialect generalization.
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6 Pith papers cite this work, alongside 11 external citations. Polarity classification is still indexing.
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Introduces LLM-friendly logical database design via three operators (+A, +P, +R) that yield up to 4.2% gains in execution accuracy on BIRD-Union and Spider-Union benchmarks.
New Text-to-Big SQL metrics show that LLM agents must balance accuracy with cost and speed at scale, where GPT-4o trades some accuracy for up to 12x speedup and GPT-5.2 proves more cost-effective than Gemini 3 Pro on large inputs.
Injecting a few vectors near the embedding-space centroid can make them appear in top-k results for up to 94.4% of queries via centrality-driven hubness.
Akashic’s MemAttention plus locality-aware placement improves agent task accuracy by up to 10.2 points and throughput by up to 1.21× over prior memory systems across four long-horizon workloads.
Progress-SQL introduces a multi-turn RL framework with ODT-based structural alignment and progressive rewards that measure improvement across refinement turns, yielding gains on BIRD, Spider, and robustness benchmarks.
citing papers explorer
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UniQL: Towards Dialect-Universal Benchmarking for Text-to-SQL
UniQL is a human-verified benchmark providing aligned natural language questions and dialect-specific SQL queries for 16 SQL systems to evaluate cross-dialect generalization.
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The Case for Text-to-SQL Friendly Logical Database Design
Introduces LLM-friendly logical database design via three operators (+A, +P, +R) that yield up to 4.2% gains in execution accuracy on BIRD-Union and Spider-Union benchmarks.
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Both Ends Count! Just How Good are LLM Agents at "Text-to-Big SQL"?
New Text-to-Big SQL metrics show that LLM agents must balance accuracy with cost and speed at scale, where GPT-4o trades some accuracy for up to 12x speedup and GPT-5.2 proves more cost-effective than Gemini 3 Pro on large inputs.
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Can You Trust the Vectors in Your Vector Database? Black-Hole Attack from Embedding Space Defects
Injecting a few vectors near the embedding-space centroid can make them appear in top-k results for up to 94.4% of queries via centrality-driven hubness.
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Akashic: A Low-Overhead LLM Inference Service with MemAttention
Akashic’s MemAttention plus locality-aware placement improves agent task accuracy by up to 10.2 points and throughput by up to 1.21× over prior memory systems across four long-horizon workloads.
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Progress-SQL: Improving Reinforcement Learning for Text-to-SQL via Progressive Rewards
Progress-SQL introduces a multi-turn RL framework with ODT-based structural alignment and progressive rewards that measure improvement across refinement turns, yielding gains on BIRD, Spider, and robustness benchmarks.