weekly-papers

Seminal Paper of the Week — the paper that quietly defined what “cloud storage” looks like from the inside. Authors: Sanjay Ghemawat, Howard Gobioff, Shun-Tak Leung (Google) Published: SOSP ‘03 — 19th ACM Symposium on Operating Systems Principles, October 2003. Canonical link: The Google File System (Google research mirror) · ACM DOI 10.1145/945445.945450 TL;DR In 2003, Ghemawat, Gobioff and Leung described how Google was running a multi-thousand-node, petabyte-scale distributed file system on commodity hardware — and how the design assumptions diverged so sharply from the established POSIX-file-system lineage that almost every architectural decision in the paper looks like a heresy until you read the workload section....

Weekly Paper Notes — one of the top picks from the 2026-06-13 CS paper digest. Area: Operating Systems / Systems. Authors: Zhuoping Yang, Yiyu Shi, Alex Jones arXiv: 2606.06697 · PDF TL;DR The GPU has quietly become a multi-tenant device — applications no longer just dispatch compute kernels, they call into vendor libraries (cuFFT, cuBLAS, NCCL), interact with GPU-resident services, and touch storage and network adapters through GPUDirect paths. But the CUDA programming model still hands each process the full keys to the device: its own context, raw device pointers, runtime handles, module loader, and direct kernel launch....

Weekly Paper Notes — one of the top picks from the 2026-06-13 CS paper digest. Area: Distributed Computing. Authors: Samuel Erickson, Mikael Johansson (KTH) arXiv: 2606.13287 · PDF TL;DR In asynchronous SGD (ASGD), workers compute gradients on possibly stale parameters and push updates without waiting for slow peers. That’s how you keep all the GPUs busy, but it’s also how slow workers (“stragglers”) inject large delays into the update stream, which classical analyses say should slow convergence in proportion to the maximum delay across the workers....

Seminal Paper of the Week — a foundational systems paper that quietly shapes how every distributed system you use is layered. Authors: Jerome H. Saltzer, David P. Reed, David D. Clark (MIT) Published: ACM Transactions on Computer Systems 2(4), November 1984. Canonical link: End-to-End Arguments in System Design (MIT) · ACM DOI 10.1145/357401.357402 TL;DR The end-to-end argument is a layering principle: a function should be implemented in a lower layer of a system only when it can be completely and correctly implemented at that layer, and when implementing it there provides a clear performance benefit over implementing it at the endpoints....

Weekly Paper Notes — Seminal Paper of the Week for the 2026-06-06 CS paper digest. Area: Distributed Systems / Databases. Citation: Giuseppe DeCandia, Deniz Hastorun, Madan Jampani, Gunavardhan Kakulapati, Avinash Lakshman, Alex Pilchin, Swaminathan Sivasubramanian, Peter Vosshall, Werner Vogels — Dynamo: Amazon’s Highly Available Key-value Store. SOSP ‘07. DOI: 10.1145/1294261.1294281 Canonical PDF: Amazon Dynamo paper (Werner Vogels’ archive) Why the paper still matters Almost every popular “NoSQL” key-value store of the last fifteen years — Cassandra, Riak, Voldemort, DynamoDB (the service), early versions of Redis Cluster, parts of MongoDB’s replica routing — pulls its core design vocabulary directly from Dynamo: consistent hashing for partitioning, vector clocks for divergence tracking, sloppy quorums with hinted handoff for availability under failure, and read repair / Merkle-tree anti-entropy for eventual convergence....

Weekly Paper Notes — one of the top picks from the 2026-06-06 CS paper digest. Area: AI / ML. Authors: Akarsh Kumar, Phillip Isola (MIT) arXiv: 2606.06479 · PDF TL;DR This paper proposes Supervised Memory Training (SMT), a way to pretrain nonlinear RNNs without ever doing backpropagation through time (BPTT). The trick: replace recurrent credit assignment with a supervised problem over memory transitions. A Transformer-based “memory encoder” is first trained with a predictive-state objective — it learns a representation m_t that retains exactly the information about the past needed to predict the future....

Weekly Paper Notes — one of the top picks from the 2026-06-06 CS paper digest. Area: NLP / Systems-for-ML. Authors: Yutao Sun, Yanqi Zhang, Li Dong, et al. (Microsoft Research Asia) arXiv: 2606.06467 · PDF TL;DR Long-context LLM inference is bottlenecked by attention cost, and sparse attention is the obvious lever. The two existing families both disappoint in practice: block-sparse patterns (sliding window, dilated, etc.) give clean speedups but lose quality, while token-sparse patterns (top-k over the KV cache) preserve quality but spend most of the budget deciding which tokens to attend to — the routing itself becomes the bottleneck....

Weekly Paper Notes — Seminal Paper of the Week for May 24–30, 2026. After a multi-week streak of systems classics (Raft, MapReduce, Lamport, ARIES), this week rotates to AI / ML. Authors: Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin (Google Brain / Google Research / University of Toronto) Venue: NeurIPS 2017 arXiv: 1706.03762 · PDF Why this paper Picking Attention Is All You Need as a Seminal Paper of the Week in 2026 feels almost too on-the-nose — the Transformer is the architectural substrate underneath every frontier LLM, every modern diffusion model, every state-of-the-art protein folding system, every reasoning model whose chain-of-thought you have ever read....

Weekly Paper Notes — one of the top picks from the May 24–30, 2026 CS paper digest. Area: NLP / Theory. Authors: Jon Kleinberg, Anay Mehrotra, Amin Saberi (Cornell / Yale / Stanford) arXiv: 2605.30324 · PDF TL;DR A line of theoretical work asks: given examples from an unknown target language drawn from a known countable collection, can a learner eventually output only new valid strings from that language? Prior results — including Kleinberg & Mullainathan’s 2024 paper that triggered the modern wave — assume the learner remembers the entire example history....

Weekly Paper Notes — one of the top picks from the May 24–30, 2026 CS paper digest. Area: AI / ML. Authors: Lukas Aichberger, Sepp Hochreiter (JKU Linz / NXAI) arXiv: 2605.30343 · PDF TL;DR Modern reasoning LLMs scale test-time compute by emitting long chains of thought — but every “thought token” is forced to round-trip through the autoregressive decoder, conflating internal computation with external communication. Reasoning in Memory (RiM) instead inserts blocks of fixed special tokens that act as scratch space for the model’s working memory....