Rohan Pandey @khoomeik
— quoting @LiamFedus — saved image
samsja reposted Rohan Pandey [verified] @khoomeik · 9h We midtrain + RL'd a trillion param LLM to analyze experimental data from our superconductor lab It's better at it than Astra & Fable, and our scientists love it Read our first research report below [embedded chart: "Automating Scientific Analysis: Frontier XRD"; y-axis Success rate (%), 0-60; x-axis Average Cost per Problem (USD), $0-$9; series labelled Periodic Neon (low, medium, high, xhigh; starred, "Neon's Base (1-trillion parameter)"), GPT-6 Astra (low to max), Claude Fable 5.1 (low to max), GPT-5.6-Sol (high to max), Claude Opus 5 (high to max), Kimi K3] [quoted post] Liam Fedus [verified] @LiamFedus · 10h [video thumbnail 0:16] We built high-throughput materials labs in Menlo Park to create a loop between experiments and models. The labs generate fresh data, the models learn from it, and then help ... [cut off]
Note from Claude Sonnet 5
X post by Rohan Pandey (@khoomeik) announcing a midtrained and RL'd trillion-parameter LLM for analyzing superconductor-lab experimental data, claimed better than Astra and Fable, quoting Liam Fedus on high-throughput materials labs in Menlo Park. The embedded chart 'Automating Scientific Analysis: Frontier XRD' plots success rate (about 20-55%) against average cost per problem ($0-$9): the starred Periodic Neon 1T model (black line) sits highest and furthest left, rising from about 36% at low to about 55% at xhigh at under $4, above GPT-6 Astra (blue, up to about 53% at max), Claude Fable 5.1 (orange, roughly 33-40%), GPT-5.6-Sol (dashed blue) and Claude Opus 5 (dashed orange), with Kimi K3 a single point near 18% (values read off the plot by eye).
periodic neonxrdscientific analysisai for sciencesuperconductorscost vs performanceastraclaude fable 5.1kimi k3materials lab