It’s less about proof and more about demonstrating a new capability that TSLMs enable. To be fair, the paper did test standard LLMs, which consistently underperformed. @iLoveOncall, can you point to examples where out of the box models achieved good results on multiple time-series? Also, what kind of time-series data did you analyze with Claude 3.5? What exactly did you predict, and how did you assess reasoning capabilities?

Thanks for the note. Ironically, the post is about models built to understand time.

If you know the trick to getting reviewed in a day, do tell. Asking for an entire field.

So you want to jump the queue?

inspired by friends at Browser-Use

whenever you would like to have comprehensive feedback on your manuscript (more likely during pre-submission or after publishing a preprint).

noted.

We also provide feedback on rigor across 7 different categories: https://github.com/robertjakob/rigorous/tree/main/Agent1_Pee...

System prompts / review criteria cannot be "leaked" because they are open-source (full transparency). Focusing heavily on monetization at this stage seems shortsighted...this tool is a small (but longterm important) step of a larger plan.

As mentioned above, there is an open-source version for those who want full control. The free cloud version is mainly for convenience and faster iteration. We don’t store manuscript files longer than necessary to generate feedback (https://www.rigorous.company/privacy), and we have no intention of using manuscripts for anything beyond testing the AI reviewer.

Here is a description of how it works: https://github.com/robertjakob/rigorous/tree/main/Agent1_Pee...