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论文提出 HybridResearcher，一个 4B 小型深度研究智能体，通过合成跨源轨迹与强化学习在结构化与非结构化数据之间进行推理，在 HybridDeepResearch 基准上超越更大的 Qwen3.5-397B-A17B，同时不遗忘单源能力。
NVIDIA's Nemotron 3.5 Super VL is now available on the River API for inference, finetuning, and RL, with claims that it outperforms Kimi K2.6 on text-to-SQL evaluations even before training and can be trained to beat Astra and Opus 5.5.
HERO 提出结合离线与测试时的 harness 优化方法，通过执行验证的成功与失败样本来学习可复用的 prompt 更新，并在测试时利用执行反馈对生成代码进行精调，在 SQL、Cypher 和 Python 基准上将 pass@1 提升约 9.6 个点。
DSABench 提出了一个评估多数据库智能体源发现、协调与执行能力的基准，包含815个人工审核任务，发现当前模型在多数据库工作流中的整体成功率仍然很低（最高约17%），凸显了跨库信息传递的挑战。
一条引用推文，讨论一项关于在部署执行harness内训练模型的研究：Qwen3-14B在与部署一致的执行harness中训练后，Spider 2.0-SQLite基准从22.2%提升至54.8%，凸显自定义harness的重要性。
The paper introduces a leakage-aware framework for conversational task disambiguation over tabular data, decomposing agents into asking and solution policies, and presents AmbiTab, a benchmark suite unifying six ambiguous text-to-SQL datasets. It also trains an asking policy with reinforcement learning, improving disambiguation metrics on all six datasets and task success on five.
论文提出 ARCS（Ambiguity Resolution Corpus for SQL），首个包含真实数据库上自然歧义的 text-to-SQL 基准，并提出“结构化消歧”范式取代自由对话澄清。实验表明歧义下 text-to-SQL 仍具挑战：GPT-6 SOTA 仅 51% 端到端执行准确率，开源模型均不超过 27%。
UCL researchers present Text2Dashboard, a governed agent architecture that converts natural-language requests into inspectable dashboards over enterprise data, combining schema-constrained LLM decisions with typed tools, deterministic hooks, and human approval gates. Evaluations on a small set of DataBrain tasks and fault scenarios show promising but limited results.
该论文提出PlanPool方法，通过将澄清计划外部化为可变问题池，解决agentic Text-to-SQL系统中过早终止澄清的问题，从而减少静默失败并提升歧义覆盖能力。
该论文提出 FD-SCoPE 框架，利用可验证、可反馈的语言模型与可执行查询，回答临床医生对临床试验证据表格的提问，包括无法直接通过数据库查询得出的派生属性问题，并在肿瘤学免疫检查点抑制剂试验数据上验证其可审计性与有效性。
Argo-Bench introduces an evaluation framework of 210 enterprise-scale data science tasks built on a simulated food-delivery platform with an ERP warehouse of 235 tables and 7.5 billion rows, testing agents that must navigate and act rather than just generate SQL. The strongest of 14 frontier and open-weight models averages only 59.5 points, highlighting a significant gap in real-world data agent capabilities.
A developer is building an agentic Text-to-SQL pipeline for querying financial databases and seeks community feedback on the architecture's robustness, security, and performance.
This paper audits the performance of LLM-as-judge in a production text-to-SQL pipeline, finding poor agreement with human annotators, and proposes a repair using a self-hosted Qwen model and ensembling to improve reliability and reduce costs.
ModularSQL introduces a runtime guardrail to address the multiplicity blind spot in Text-to-SQL systems, detecting and correcting multiplicity errors with low overhead to improve execution safety in production.
Coal-SQL is a post-training framework for Text-to-SQL that uses coverage-guided augmentation and failure-driven learning to improve SQL generation, achieving 64.9% execution accuracy on the BIRD benchmark with only 12.6K examples.
The article describes deploying an AI text-to-sql system for a bank, highlighting that the model was less important than verification mechanisms, evaluation sets, and governance rules for production success.
DualSQL proposes a multi-agent reinforcement learning framework for Text-to-SQL, using a single shared model to jointly optimize schema linking and SQL generation, achieving state-of-the-art accuracy with smaller model sizes.
WrenAI is an open-source generative BI engine that allows users to ask questions in natural language to generate SQL, charts, and analysis results, and it features a semantic layer to enhance accuracy.
text2ql is an open-source Python framework for multi-target natural language querying via a language-agnostic intermediate representation, supporting SQL and GraphQL with LLM-backed and deterministic modes, achieving high accuracy on benchmarks.
This paper introduces a production enterprise analytics system that inverts the interaction model from question-first to analyst-first through domain-expert skills and knowledge compilation, enabling proactive analytics with verified metrics and suggested questions.