Analytical workloads operating on data stored in external database systems face a fundamental bottleneck: data access is guarded entirely by the database driver, like JDBC or ODBC, forcing all reads through query execution and other driver layers that are not designed for bulk columnar analytics. We present Jailbreak, an approach that bypasses the database engine entirely by reading storage files directly and materializing data as in-memory columnar buffers.
arXiv:2607. 07696v1 Announce Type: cross Abstract: Analytical workloads operating on data stored in external database systems face a fundamental bottleneck: data access is guarded entirely by the database driver, like JDBC or ODBC, forcing all reads through query execution and other driver layers that are not designed for bulk columnar analytics.
Zeta‑Lite is a WebAssembly‑based in‑browser SQL database that brings concurrent, snapshot‑isolated transactions and copy‑on‑write database branching to the client side. It is a compact 2.87 MB gzipped build of the Zeta engine, offering a full PostgreSQL‑compatible feature set—including joins, CTEs, window functions, JSONB, full‑text search, HNSW vector search, and graph queries—while maintaining high read/write throughput across major browsers. The engine’s log‑centric asynchronous MVCC core enables overlapping transactions on a single thread and unique branching capabilities rarely seen even in server‑side databases. whyItMatters":"Zeta‑Lite’s concurrent, branchable design provides a lightweight, privacy‑preserving, and offline‑ready memory layer for in‑browser AI agents, enabling them to explore, test, and commit speculative changes efficiently."
arXiv:2607. 20630v1 Announce Type: cross Abstract: Traditional query processing engines require continuous development and extensions to support new techniques and user requirements, and in some cases, entirely new systems must be built from scratch.
arXiv:2608.22795v1 Announce Type: new Abstract: The AI field has been rapidly developing, leading to the emergence of a large number of AI training datasets of various types. These datasets contain d...
The paper introduces a new text‑to‑SQL benchmarking framework that incorporates realistic role‑based access control (RBAC) constraints. It augments existing benchmarks by generating plausible user roles and access policies through an LLM‑assisted workflow, followed by human‑in‑the‑loop quality control. The framework also provides evaluation metrics to detect RBAC‑specific failures and separate SQL utility from compliance, revealing that many high‑scoring models degrade sharply under access constraints.
arXiv:2606. 05679v1 Announce Type: cross Abstract: Agents increasingly generate SQL, orchestrate pipelines, and automate data analysis on behalf of users.
arXiv:2606. 31808v1 Announce Type: new Abstract: Language model systems built around proprietary APIs often operate on a token-based cost model.
arXiv:2607. 17269v1 Announce Type: new Abstract: Large language models encode world models implicitly in neural weights, which exposes four structural risks in high-precision domains such as medicine and finance: hallucination, frozen knowledge, poor explainability, and poor modifiability.
Agents increasingly generate SQL, orchestrate pipelines, and automate data analysis on behalf of users. While recent work improves query correctness, correctness is not safety.
arXiv:2601. 16956v1 Announce Type: cross Abstract: The rapid growth of Large Transformer-based models, specifically Large Language Models (LLMs), now scaling to trillions of parameters, has necessitated training across thousands of GPUs using complex hybrid parallelism strategies (e.
arXiv:2502. 11201v3 Announce Type: replace-cross Abstract: NoSQL databases are core data infrastructure, yet natural-language access to them remains underdeveloped: correct query generation must recover how a non-relational data model represents entities, nested paths, arrays, missing fields, and dynamic keys.
Practical AI systems increasingly need to turn long, heterogeneous documents into queryable relational databases, not isolated spreadsheets. In domains such as finance, healthcare, education, transportation, and enterprise operations, downstream workflows rely on normalized schemas, entity identities, keys, cross-table relationships, and integrity constraints for analytics, compliance, auditing, and SQL-backed decision making.