arXiv AI By Gene Zhang

Zeta-Lite: A Concurrent, Branchable In-Browser SQL Database for Agentic Memory

Read the original on arXiv AI →

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."

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Jul 9

Breaking Database Lock-in: Agentic Regeneration of High Performance Storage Readers for Database Bypass

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.

By Victor Giannakouris, Immanuel Trummer
Hugging Face Trending Papers
Jul 8

Breaking Database Lock-in: Agentic Regeneration of High Performance Storage Readers for Database Bypass

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 AI
Jun 30

Experience Graphs: The Data Foundation for Self-Improving Agents

arXiv:2606. 29823v1 Announce Type: cross Abstract: The database community has repeatedly advanced the state of the art by recognizing that new workloads demand new system architectures.

By Gang Liao, Yujia He, Abdullah Ozturk, Zhouyang Li, Ying Wang, Zhitong Guo, Hongsen Qin, Yaobin Qin, Tao Yang, Zewei Jiang, Dianshi Li, Jort Gemmeke, Jiangyuan Li, Liyuan Li, Nathan Yan, Masha Basmanova, Uladzimir Pashkevich, Matt Steiner, Pedro Pedreira, Rob Fergus, Anirudh Goyal, Carole-Jean Wu, Gaoxiang Liu, Andrew Witten, Daniel J. Abadi