Introducing TimeRSI, a self-improving agentic deployment system for time-series foundation models, Deqing Fu says. With TimeRSI, TimesFM-3.0 returns to No. 1 on the TIME and FEV-bench leaderboards as of October 15. Fu also says TabFM-Auto, a coding agent that evolves the pipeline around a frozen tabular foundation model, beats the field.
TabFM-Auto (https://x.com/DeqingFu/status/2105185258882887700?s=20) showed that a coding agent evolving the pipeline around a frozen tabular FM can beat the field.
Time-series foundation models need a harness, too. 🚀
Introducing TimeRSI: a self-improving agentic deployment system for TSFMs 🧵
With TimeRSI, TimesFM-3.0 returns to #1 on TIME🏆 and FEV-bench🏆 as of Oct15.
Introducing TabFM-Auto, my summer project at @GoogleResearch . LLMs possess rich world knowledge yet bad at handling massive tables with millions of number cells. Tabular Foundation Models are so good at tabular in-context learning but don't have semantic knowledges.
Source: Deqing Fu · x.comPublished · added here
