<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Attention on Arsh Chawla</title><link>https://example.org/tags/attention/</link><description>Recent content in Attention on Arsh Chawla</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>arshsecondary@gmail.com (Arsh Chawla)</managingEditor><webMaster>arshsecondary@gmail.com (Arsh Chawla)</webMaster><copyright>© 2026 Arsh Chawla</copyright><lastBuildDate>Fri, 17 Apr 2026 12:30:00 +1000</lastBuildDate><atom:link href="https://example.org/tags/attention/index.xml" rel="self" type="application/rss+xml"/><item><title>ZelusBench: Measuring LLM Attention with Geometry</title><link>https://example.org/blog/zelusbench/</link><pubDate>Fri, 17 Apr 2026 12:30:00 +1000</pubDate><author>arshsecondary@gmail.com (Arsh Chawla)</author><guid>https://example.org/blog/zelusbench/</guid><description>For the Google DeepMind AGI hackathon I took on the attention pathway—a genuinely hard thing to measure. My answer was to make relevance a mathematical fact instead of a judgement call: ZelusBench grounds attention tasks in 3D geometry, and the results reveal distinct cognitive profiles across frontier models.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://example.org/blog/zelusbench/featured.jpg"/></item></channel></rss>