What if you could see an AI think not just the final answer, but the whole stream of reasoning: every search, every dead end, every moment of insight? We’re building exactly that: a visible, measurable thought process we call the Jitter. This post the first in a series shows how we’re creating the habitat where that digital thought stream can live and grow.
We’ll draw on ideas from:
What if the most valuable insights in AI evaluation aren’t in model agreements, but in systematic disagreements?
This post reveals that the “gap” between large and small reasoning models contains structured, measurable intelligence about how different architectures reason. We demonstrate how to transform model disagreements from a problem into a solution, using the space between models to make tiny networks behave more like their heavyweight counterparts.
We start by assembling a high-quality corpus (10k–50k conversation turns), score it with a local LLM to create targets, and train both HRM and Tiny models under identical conditions. Then we run fresh documents through both models, collecting not just final scores but rich auxiliary signals (uncertainty, consistency, OOD detection, etc.) and visualize what these signals reveal.
“The medium is the message.” Marshall McLuhan
We took him literally.
What if you could literally watch an AI think not through confusing graphs or logs, but by seeing its reasoning process, frame by frame? Right now, AI decisions are black boxes. When your medical device rejects a treatment, your security system flags a false positive, or your recommendation engine fails catastrophically you get no explanation, just a ’trust me’ from a $10M model. ZeroModel changes this forever.