The Capability Induction Framework: A Systems Approach to LLM Development

First, I want to say that I definitely wrote this technical paper with the use of LLMs. Multiple, as a matter of fact. Using models to assist while I brainstorm with them to find more efficient training methods is the sort of meta process I absolutely love to use these tools for.

Every idea in this document is mine, from the developmental psychology parallels, the scantron curriculum, the emergent disposition principle, and the argument that rewarding a model for challenging its own evaluators is the structural inverse of sycophancy. But since I’m not an ML engineer, I figured this would be a good test to ensure the model “understands” what I meant. I asked it to draft this concept using language that is more aligned with industry standards and it delivered. Remarkably, the ideas didn’t change, but what did crystallize is why the current methods are so utterly cursed.

One last thing – while drafting this paper, I often had to stop the model mid-process and ask it to review its last few turns for sycophancy. Naturally, it found some. Had this framework been in place, it would have fixed the need for that correction in the first place and saved me sooooo many tokens.

The full paper — The Capability Induction Framework: A Systems Approach to LLM Development — is published and citable at https://doi.org/10.5281/zenodo.21880848.