<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Research Papers — The Latent Daily</title><description>Important papers explained in plain language.</description><link>https://thelatentdaily.com/</link><language>en-us</language><atom:link href="https://thelatentdaily.com/rss/research-papers.xml" rel="self" type="application/rss+xml"/><item><title>How to read an AI research paper (and spot the hype)</title><link>https://thelatentdaily.com/articles/how-to-read-an-ai-research-paper/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/how-to-read-an-ai-research-paper/</guid><description>A practical method for reading machine learning papers: what to read first, how to judge experiments and benchmarks, and the red flags that signal overclaiming.</description><pubDate>Sun, 18 Oct 2026 05:26:00 GMT</pubDate><category>Research Papers</category><category>research</category><category>papers</category><category>how-to</category><category>benchmarks</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Scaling laws explained: why bigger language models kept getting better</title><link>https://thelatentdaily.com/research/scaling-laws-for-neural-language-models-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/scaling-laws-for-neural-language-models-explained/</guid><description>The 2020 OpenAI paper by Kaplan and colleagues showed that model loss falls predictably with size, data and compute. What it found, why it mattered and its limits.</description><pubDate>Sun, 18 Oct 2026 05:25:00 GMT</pubDate><category>Research Papers</category><category>research</category><category>scaling</category><category>llms</category><category>compute</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>LLM-as-a-judge explained: using AI models to grade AI output</title><link>https://thelatentdaily.com/articles/llm-as-a-judge-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/llm-as-a-judge-explained/</guid><description>Language models can score other models&apos; answers at scale. How LLM-as-a-judge works, its known biases, and how to design rubrics and checks that make it reliable.</description><pubDate>Sun, 18 Oct 2026 04:42:00 GMT</pubDate><category>Evals &amp; Observability</category><category>evals</category><category>llm-as-a-judge</category><category>testing</category><category>research</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How universities are responding to generative AI</title><link>https://thelatentdaily.com/articles/universities-and-generative-ai-policies/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/universities-and-generative-ai-policies/</guid><description>Academic integrity, AI disclosure rules, research use, teaching AI literacy and institutional AI tools: how higher education is adapting to ChatGPT-era students.</description><pubDate>Sun, 18 Oct 2026 04:18:00 GMT</pubDate><category>Education &amp; AI</category><category>education</category><category>universities</category><category>academic-integrity</category><category>research</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>The major AI conferences explained: NeurIPS, ICML, ICLR, CVPR and more</title><link>https://thelatentdaily.com/articles/major-ai-conferences-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/major-ai-conferences-explained/</guid><description>A guide to the top machine learning and AI research conferences, what each focuses on, how peer review works, and why conferences matter more than journals in AI.</description><pubDate>Sun, 18 Oct 2026 03:08:00 GMT</pubDate><category>Events &amp; Conferences</category><category>conferences</category><category>neurips</category><category>research</category><category>academia</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Paper of the day: Switch Transformers and the rise of mixture-of-experts</title><link>https://thelatentdaily.com/research/switch-transformers-mixture-of-experts-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/switch-transformers-mixture-of-experts-explained/</guid><description>Switch Transformers showed that routing each token to one expert lets models grow to a trillion parameters without proportional compute. How MoE works and why it matters.</description><pubDate>Sun, 18 Oct 2026 02:53:00 GMT</pubDate><category>Paper of the Day</category><category>research</category><category>mixture-of-experts</category><category>transformers</category><category>scaling</category><author>dario_admin@resolvia-crm.tech</author></item></channel></rss>