SAGE (Self-Adaptive Generative Execution) introduces a unified framework for integrating AI functions into SQL by defining three typed primitives—AI_SCALAR, AI_AGG, and AI_JOIN—that correspond to the relational roles of transforming rows, aggregating groups, and joining row pairs. The framework standardizes a confidence-gated execution interface and tailors physical strategies to each primitive’s shape, with AI_JOIN employing predicate analysis and a recipe card to select optimal execution plans. Evaluations across scalar, aggregate, and join workloads demonstrate that SAGE consistently improves execution quality and efficiency, achieving the best overall SemBench performance and dramatically reducing model calls in factorable joins.
By Xiangqi Wang, Nhan H. Pham, Oktie Hassanzadeh, Dharmashankar Subramanian, Xiangliang Zhang
The paper introduces a new Monte Carlo algorithm for approximate counting of Disjunctive Normal Form (DNF) formulas, featuring an adaptive stopping rule and short‑circuit evaluation. It achieves PAC learning bounds and is asymptotically more efficient than existing methods, including classical Monte Carlo, hashing‑based, and neural‑network approaches. Experiments demonstrate that the algorithm outperforms prior techniques by orders of magnitude and scales to problems with millions of variables.
By Paul Burkhardt, David G. Harris, Kevin T Schmitt
arXiv:2607. 03926v1 Announce Type: cross Abstract: Synthetic tabular data support use cases like data sharing, model development under access restrictions, and rapid prototyping of analytical workflows.
By Jialin Zhang, Fenghao Dong, Yajie Zhou, Vyas Sekar, Shinan Liu
arXiv:2606. 20227v1 Announce Type: new Abstract: Large Language Models (LLMs) have made significant progress in reasoning, particularly in deductive reasoning, which is crucial for high-stakes decision-making.
By Xinyi Zheng, Ling Shi, Tianlong Yu, Yongxin Zhao, Lorenz Goette, Kailong Wang
arXiv:2509. 00930v2 Announce Type: replace Abstract: Large language models (LLMs) exhibit strong general reasoning, yet the community lacks controllable, scalable, and verifiable tools to analyze and improve these abilities.
By Yanxiao Zhao, Yaqian Li, Zihao Bo, Rinyoichi Takezoe, Haojia Hui, Mo Guang, Lei Ren, Xiaolin Qin, Kaiwen Long
arXiv:2606. 11946v1 Announce Type: cross Abstract: The conventional approach to deep learning over relational databases applies neural models, such as Graph Neural Networks (GNNs), to a graph representation of the database.
By Arie Soeteman, Balder ten Cate, Maurice Funk, Benny Kimelfeld, Carsten Lutz, Moritz Sch\"onherr