<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>MiRAGE: A Multiagent Framework for Generating Multimodal Multihop Question-Answer Dataset for RAG Evaluation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2601.15487/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:27:26 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2601.15487/feed.xml" rel="self" type="application/rss+xml" /><item><title>MiRAGE: A Multiagent Framework for Generating Multimodal Multihop Question-Answer Dataset for RAG Evaluation — HN submission</title><link>https://news.ycombinator.com/item?id=46960968</link><guid isPermaLink="false">urn:hackerpapers:submission:2601.15487:46960968</guid><pubDate>Tue, 10 Feb 2026 15:24:52 GMT</pubDate><description>&lt;p&gt;MiRAGE: Open-source framework for multimodal RAG evaluation&lt;/p&gt;&lt;p&gt;Code: https://github.com/ChandanKSahu/MiRAGE Hi HN, we are the authors of MiRAGE. We built this because standard RAG benchmarks (like Natural Questions) rely on text-only Wikipedia-like data, which doesn&amp;#x27;t reflect the reality of enterprise RAG. In the real world, &amp;quot;truth&amp;quot; is often locked in a chart, a complex table, or a diagram deep inside a PDF. MiRAGE is an open-source framework that uses a swarm of specialized agents to reverse-engineer evaluation datasets from your own documents. How it works: 1. Ingest: It uses vision models to describe charts/tables and &amp;quot;semantically chunk&amp;quot; the PDF. 2.…&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2601.15487/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
