Three papers this week — one seminal classic, two picks from the last seven days of arXiv submissions across AI/ML, NLP, distributed systems, databases, OS, networking, and PL.
🔁 Seminal Paper of the Week#
- Paxos Made Simple — Leslie Lamport, 2001. The paper that finally made Paxos legible, and the intellectual ancestor of Chubby, Spanner, ZooKeeper, Raft, and every serious replicated state machine in production today.
⭐ Top Papers of the Week#
The Slack Weekly CS Paper Digest covered additional notable papers across cs.AI, cs.LG, cs.CL, cs.DC, cs.DB, cs.OS, cs.NI, cs.PL, and stat.ML that were not expanded into standalone posts here. The three above are the ones that most warrant a full read.
arXiv: 2607.13276 · PDF: 2607.13276.pdf
Authors: Marc Brooker, Marc Bowes, et al. (Amazon Web Services)
TL;DR Aurora DSQL is AWS’s new serverless, PostgreSQL-compatible OLTP database designed for multi-region active-active writes. The architecture disaggregates compute (Firecracker MicroVMs running stateless SQL), storage, and transaction coordination into independent horizontally-scalable services. It uses MVCC with precision timestamps for coordination-free reads and optimistic concurrency control for writes, deferring all coordination to commit time via distributed adjudicators and a Journal replication tier....
Original: Leslie Lamport, Paxos Made Simple, ACM SIGACT News 32(4), December 2001. Canonical PDF: lamport.azurewebsites.net/pubs/paxos-simple.pdf Predecessor: The Part-Time Parliament, ACM TOCS 16(2), 1998 (the “island of Paxos” allegory that nobody could read).
Why “made simple” Lamport originally described his consensus algorithm in 1998 in The Part-Time Parliament, a paper framed as archaeological reconstruction of the parliamentary procedures of an ancient Greek island. It was a joke. It was also, by broad consensus (pun deliberate), unreadable — reviewers hated it, adoption was near zero for years, and even engineers who wanted to build on it complained they couldn’t....
arXiv: 2607.15267 · PDF: 2607.15267.pdf
Authors: Victoria Graf, Hannaneh Hajishirzi, et al.
TL;DR Prior work on pretraining-data poisoning has mostly targeted curated sources like Wikipedia — a poor stand-in for the scale and heterogeneity of real pretraining corpora. This paper demonstrates that public discussion interfaces on the open web (comment sections, forums, Q&A pages) are a viable at-scale injection vector, and introduces HalfLife, an analysis technique for estimating whether adversarial content actually survives web-crawl-based data curation pipelines and lands in the training set....