<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/">
  <channel>
    <title>Weekly CS Paper Digest — 26 September – 2 October 2026 on Sparse Notes</title>
    <link>https://sparsenotes.com/posts/2026/10/papers/</link>
    <description>Recent content in Weekly CS Paper Digest — 26 September – 2 October 2026 on Sparse Notes</description>
    <image>
      <url>https://sparsenotes.com/images/og-default.png</url>
      <link>https://sparsenotes.com/images/og-default.png</link>
    </image>
    <generator>Hugo -- gohugo.io</generator>
    <lastBuildDate>Sat, 03 Oct 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://sparsenotes.com/posts/2026/10/papers/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Congestion Avoidance and Control (Jacobson, 1988)</title>
      <link>https://sparsenotes.com/posts/2026/10/papers/congestion-avoidance-and-control-jacobson-1988/</link>
      <pubDate>Sat, 03 Oct 2026 00:00:00 +0000</pubDate>
      
      <guid>https://sparsenotes.com/posts/2026/10/papers/congestion-avoidance-and-control-jacobson-1988/</guid>
      <description>Seminal paper of the week: Van Jacobson&amp;#39;s response to the 1986 Internet congestion collapse. Packet conservation, ACK clocking, slow start, a variance-aware retransmit timer, and AIMD, all added to TCP senders without changing the protocol or a single router.</description>
    </item>
    
    <item>
      <title>Context Language Models</title>
      <link>https://sparsenotes.com/posts/2026/10/papers/context-language-models/</link>
      <pubDate>Sat, 03 Oct 2026 00:00:00 +0000</pubDate>
      
      <guid>https://sparsenotes.com/posts/2026/10/papers/context-language-models/</guid>
      <description>UW and Meta give the model write access to its own context, exposed as a file it edits with Bash. Zero-shot it beats summarisation and tool-based context managers on deep research, terminal coding, and 24-hour agent swarms, and it can be steered by prompt, evolved, or trained with RL.</description>
    </item>
    
    <item>
      <title>Mixture-of-Kittens: MoE Megakernel for NVL72s</title>
      <link>https://sparsenotes.com/posts/2026/10/papers/mixture-of-kittens-moe-megakernel-nvl72/</link>
      <pubDate>Sat, 03 Oct 2026 00:00:00 +0000</pubDate>
      
      <guid>https://sparsenotes.com/posts/2026/10/papers/mixture-of-kittens-moe-megakernel-nvl72/</guid>
      <description>Cursor and Stanford rebuild MoE training for a 72-GPU NVLink domain: pull dispatch, push combine, a tunable overlap granularity, and a device-side ring buffer, all inside one deterministic megakernel. Up to 2.37x over the best public baseline, 1.41x end-to-end on 512 GB300s.</description>
    </item>
    
  </channel>
</rss>
