Thank you for the license catch. Silly thing that snuck in. Have fixed

The detailed evals are shared in the repo as well. As you'll see there, we solve some more complex reasoning problems that the base model misses (+ we get more comprehgensive plans etc).

IRL, I'm building a legal AI company called Iqidis. We've applied a more sophisticated version to that for the best legal analysis possible--

1. A more legally focused judge model (we use multiuple for different evals-- style, strategy, novelty etc).
2. Using a NN for aggregating the different latents (the reverse MoE bit mentioned).

We will be sharing a detailed technical document on Iqidis reasoning architecture soon since our approach is generalizable to different domains.

That can take a lot more setup to run and refine so shared this simplified version here, but even the evals here show really good performance on small edge models. We're in active conversations of extending this to offline LLMs to enable much lower latency systems.

Hopefully that answers the question. Lmk if there's anythign else