weekly-papers

Weekly Paper Notes — 🔁 Seminal Paper of the Week for the 2026-07-25 CS paper digest. Area: Distributed Computing. Authors: Leslie Lamport, Robert Shostak, Marshall Pease (SRI International) Venue: ACM Transactions on Programming Languages and Systems, Vol. 4, No. 3, July 1982, pp. 382–401. DOI: 10.1145/357172.357176 · PDF (SRI copy) Why the paper still matters Almost every distributed system in production today — Spanner, etcd, ZooKeeper, Kafka, every blockchain, every consensus protocol with a Greek letter in its name — is a descendant of the impossibility and possibility results in this 20-page paper....

Weekly Paper Notes — one of the top picks from the 2026-07-25 CS paper digest. Area: NLP / LLM Inference. Author: Alagappan Valliappan arXiv: 2607.21535 · PDF TL;DR Frontier LLMs increasingly ship a built-in Multi-Token-Prediction (MTP / NEXTN) draft head for speculative decoding, based on the assumption that the draft is negligibly cheap. Windowed-MTP shows that assumption breaks catastrophically at million-token context: the native MTP head does full attention over the entire KV cache at every draft step, so its cost grows linearly with context and comes to dominate — precisely where speculation is supposed to matter most....

Weekly Paper Notes — one of the top picks from the 2026-07-11 CS paper digest. Area: AI / ML. Authors: Shreyas Subramanian, Adewale Akinfaderin, Akarsha Sehwag (Amazon) arXiv: 2607.08733 · PDF TL;DR “Super Weights” — individual scalar parameters in a large language model whose removal collapses task accuracy — were the interpretability finding of 2024–2025. The natural inference was that if these coordinates matter that much for the forward pass, they should also matter that much for learning: freeze everything else, train only the Super Weights (or a small neighbourhood around them), and you should get parameter-efficient fine-tuning for essentially free....

Weekly Paper Notes — Seminal Paper of the Week for the 2026-07-11 CS paper digest. Area: Distributed Computing / Coordination. Author: Mike Burrows (Google) Venue: OSDI ‘06 — The Chubby lock service for loosely-coupled distributed systems Canonical link: OSDI ‘06 proceedings · PDF Why the paper still matters Twenty years after publication, Chubby is the paper you can point at to explain almost every coordination system in the modern datacenter....

Weekly Paper Notes — one of the top picks from the 2026-07-11 CS paper digest. Area: Distributed Computing / Systems. Authors: Hannah Atmer, Thiemo Voigt, Yuan Yao, Stefanos Kaxiras (Uppsala University) arXiv: 2607.08407 · PDF TL;DR DRAM is the choke point of the current generative-AI buildout. HBM3e stacks are backordered, contract prices are up, and hyperscalers are absorbing a large fraction of global DRAM output just to fan the same model weights out to ever more accelerators....

Weekly Paper Notes — Seminal Paper of the Week for the 2026-06-27 CS paper digest. Area: Distributed Computing. Author: Satoshi Nakamoto Published: October 31, 2008 (cryptography mailing list) Canonical URL: bitcoin.org/bitcoin.pdf This week’s digest had a fresh paper on transaction-fair ordering on DAGs without weak edges (Tilikum, 2606.27250) — a piece of work whose entire problem statement (DeFi, BEV, reordering attacks) only exists because of one nine-page document published seventeen-and-a-half years ago to a small cryptography mailing list....

Weekly Paper Notes — one of the top picks from the 2026-06-27 CS paper digest. Area: AI / ML. Authors: Nicklas Hansen, Xiaolong Wang (UC San Diego) arXiv: 2606.27326 · PDF · Interactive TL;DR Generative action-conditional world models produce visually fluent rollouts that drift from the true dynamics — the “fluent but wrong” failure mode familiar from LLMs, now in video. Hansen and Wang argue this is fundamentally a data coverage problem: hallucinations concentrate in regions of state-action space the training set under-samples, and cheap data-centric signals computed at inference time can both detect and prevent them....

Weekly Paper Notes — one of the top picks from the 2026-06-27 CS paper digest. Area: AI / ML. Authors: Josef Chen arXiv: 2606.27288 · PDF TL;DR The paper formalizes a hard accuracy ceiling for any multi-model LLM system whose final output is one member’s answer — routing, voting, cascades, fusion, mixture-of-agents. That ceiling is 1 − β, where β is the co-failure rate: the fraction of queries on which every model in the pool is wrong simultaneously....

Weekly Paper Notes — one of the top picks from the 2026-06-20 CS paper digest. Area: Distributed Computing. Authors: Jiasheng Zhou, Longbin Zeng, Clavis Chen, Ruiming Lu et al. arXiv: 2606.20374 · PDF TL;DR ARGUS is a tracing and performance-diagnosis system designed for always-on operation on production LLM training clusters with more than 10,000 GPUs. The central insight is that no single profiler can be cheap, deep, and continuous all at once — so ARGUS decomposes observation along the training call hierarchy into three independent collection channels: CPU call stacks, framework semantics, and GPU kernel execution....

Weekly Paper Notes — one of the top picks from the 2026-06-20 CS paper digest. Area: Databases / Systems. Authors: Georg Kreuzmayr (TigerBeetle), Muhammad El-Hindi (TUM), Benjamin Wagner (Firebolt), Tobias Ziegler (TigerBeetle), Viktor Leis (TUM) arXiv: 2606.19969 · PDF TL;DR For two decades distributed database systems treated the network as an opaque, kernel-managed pipe and the kernel TCP stack was fast enough that this abstraction was free. It isn’t anymore....