构建文本转SQL的自主性分类体系与实证基准，统一评估大模型性能。
Agentic-SQL Revisited: Autonomy-Based Taxonomy and Empirical Benchmark Analysis for LLM Text-to-SQL
- 按推理自主性从弱到强划分五类生成模式，确保评估可追溯。
- 8B开源模型在难例上表现提升，但高自主性带来计算成本上升。
- 链式思考监督主要提升复杂查询准确率，适合进阶研究者参考。
基于大语言模型的文本转SQL研究在异构基准、模型架构和推理协议中进展纷呈，导致跨系统比较困难。本文将其重构为排行榜聚合：收集作者自报指标，并沿推理自主性轴（受限、上下文、迭代、自主代理、内部推理）组织数据，每项结果均可溯源。为锚定评估，我们在Spider数据集上开展聚焦案例研究，对比8B级开源模型在有无思维链（CoT）监督下与少量样本的DeepSeek-V3及GLM-4基线的表现。发现四个规律：Spider在BIRD和Spider-2.0上的迁移能力不均；自主性提升带来鲁棒性但伴随显著代价；内部推理介于仅输出答案与外部调度代理之间；思维链收益集中于难例（Hard）与超难例（Extra-Hard）。我们发布一个Python工具包，映射该自主性轴，未来方法可直接接入排行榜。
原文摘要 · Abstract (English)
LLM-based Text-to-SQL progress is reported across heterogeneous benchmarks, backbones, and inference protocols, making cross-system comparison fragile. We reframe the field as a leaderboard aggregation: we collect the metrics authors themselves report and organize them along an inference-autonomy axis spanning constrained, in-context, iterative, agentic, and reasoning-internalized generation, with traceable provenance for every cell. To anchor the aggregation empirically, we run a focused case study on Spider, comparing 8B open-source backbones with and without chain-of-thought (CoT) supervision against few-shot DeepSeek~V3 and GLM-4 baselines. Four patterns emerge: Spider gains transfer unevenly to BIRD and Spider~2.0; autonomy buys robustness at non-trivial cost; reasoning internalization sits between answer-only decoding and externally orchestrated agents; and CoT gains concentrate on Hard and Extra-Hard queries. We release a Python harness mirroring the autonomy axis so that future methods can be added directly to the leaderboard.
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