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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Autonomous LLM-driven research from data to human-verifiable research papers · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2404.17605/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2404.17605/feed.xml" rel="self" type="application/rss+xml" /><item><title>Autonomous LLM-driven research from data to human-verifiable research papers — mentioned by roykishony</title><link>https://news.ycombinator.com/item?id=40336161</link><guid isPermaLink="false">urn:hackerpapers:mention:2404.17605:40336161</guid><pubDate>Sun, 12 May 2024 17:47:15 GMT</pubDate><description>&lt;p&gt;Thanks so much for these thorough comments. You suggested some directions for more complex analysis that could be done on this data - I would be so curious to see what you get if you could take the time to try out running data-to-paper as a co-pilot on your own - you can then give it directions and feedback on where to go - will be fascinating to see where you take it! We also must look ahead: complexity and novelty will rapidly increase as ChatGPT5, ChatGPT6 etc are rolled in. The key with data-to-paper is to build a platform that harnesses these tools in a structured way that creates…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2404.17605/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>q-bio.OT</category></item><item><title>Autonomous LLM-driven research from data to human-verifiable research papers — mentioned by ttaallooss</title><link>https://news.ycombinator.com/item?id=40334778</link><guid isPermaLink="false">urn:hackerpapers:mention:2404.17605:40334778</guid><pubDate>Sun, 12 May 2024 14:31:50 GMT</pubDate><description>&lt;p&gt;I encourage you to look at the manuscript we have put on arXiv: https://arxiv.org/abs/2404.17605 and go through the thread on X: https://x.com/RoyKishony/status/1785319021329674593 We will be happy to explain and even correct ourselves, if needed, if approached in a civil, respectful manner.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2404.17605/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>q-bio.OT</category></item><item><title>Autonomous LLM-driven research from data to human-verifiable research papers — mentioned by roykishony</title><link>https://news.ycombinator.com/item?id=40334645</link><guid isPermaLink="false">urn:hackerpapers:mention:2404.17605:40334645</guid><pubDate>Sun, 12 May 2024 14:12:41 GMT</pubDate><description>&lt;p&gt;Thanks everyone for engagement and discussion. Following the range of comments, just a few thoughts: 1. Traceability, transparency and verifiability. I think the key question for me is not only whether AI can accelerate science, but rather how we can use AI to accelerate science while at the same time enhancing key scientific values, like transparency, traceability and verifiability. More and more these days when I read scientific papers, published either at high impact journals or at more specialized journals, I find it so hard, and sometimes even frustratingly impossible, to understand and…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2404.17605/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>q-bio.OT</category></item></channel></rss>
