In a new article published today on the cover of @CellCellPress, we obtained Liquid Foundation Model instances that establish state-of-the-art performance on biological longevity tasks, outperforming the best frontier models such as Gemini-3.1-Pro, GPT-5, and Claude Opus. In partnership with @InSilicoMeds, we built and released: > A comprehensive eval suite of 17 biological longevity tasks (i.e., LongevityBench), to assess whether a general-purpose language model can interpret aging data spanning clinical records, DNA methylation, transcriptomics, plasma proteomics, and genetic evidence. > LFM2-1.2B-Longevity and LFM2-2.6B-Longevity: two compact models specialized for interpreting structured aging data across these tasks. These results are important! 🧵