weekly-papers-2026-11-07

Three SMR architectures. Conventional: clients talk to servers that run consensus and hold materialized state. Disaggregated: servers hold state but the log is on external storage. Serverless (S2MR): clients talk directly to external storage holding both the log and a single copy of state.

Eliminating Servers in State Machine Replication

Weekly Paper Notes — one of the top picks from the 2026-11-07 CS paper digest. Area: Distributed Computing / Databases. Authors: Dohyun Park (UIUC), Wenjie Hu (UW–Madison), Xiangyao Yu (UW–Madison), Mahesh Balakrishnan (Meta; work done prior), Yongjoo Park (UIUC) arXiv: 2611.01363 · PDF TL;DR State machine replication (SMR) is how etcd, ZooKeeper, Spanner, CockroachDB and their relatives keep state consistent: agree on a total order of commands, then have servers apply them....

Top: a benchmark turned into easier versions by truncation, masking, or logit mixing at hint level H. Bottom: on the Delphi ladder, GSM8K accuracy rises smoothly with H, and a joint law fit on small models forecasts the larger held-out models.

Hinted Scaling Laws

Weekly Paper Notes — one of the top picks from the 2026-11-07 CS paper digest. Area: AI / ML. Authors: Suze van Adrichem, Luke Bailey, Rohith Kuditipudi (equal contribution), Christine Ye, Neil Band, Ahmed Ahmed, John Duchi, Percy Liang, Tatsunori Hashimoto (Stanford University) arXiv: 2611.02270 · PDF · Code TL;DR Scaling laws on pretraining loss work. Scaling laws on benchmark accuracy mostly don’t, because the small models you can afford to train score zero on hard benchmarks, and a row of zeros gives you no trend to extrapolate....

Learning Representations by Back-propagating Errors (Rumelhart, Hinton & Williams, 1986)

Weekly Paper Notes — the Seminal Paper of the Week for the 2026-11-07 digest. Area: AI / ML. Authors: David E. Rumelhart (UC San Diego), Geoffrey E. Hinton (Carnegie Mellon University), Ronald J. Williams (UC San Diego) Published: Nature 323, 533–536, 9 October 1986. DOI: 10.1038/323533a0 Why the paper still matters Every model in this week’s digest was trained by the procedure this letter describes: run the network forward, measure the error, push its derivative backward layer by layer, and nudge every weight downhill....