Small language models are attractive because they're cheap and fast. The problem is hallucination — and specifically, what happens when a model gets stuck in a self-validation loop: it invents a fact, then treats its own invention as ground truth for the next step of reasoning.
This week, as an R&D experiment, we paired an ultra-lightweight model (OPT-125M) with HUQAN's agentic framework layer to see whether a "trust hierarchy" on top of a tiny model could catch that failure mode before it reaches the user. Here's what we found — with the numbers, not just the pitch.
The core idea: an Evidence-Ranker instead of a bigger model
Instead of trying to make the model itself smarter, HUQAN sits on top of it and scores where a claim came from, not just what it says. Every piece of information gets a baseline trust score based on its source:
Source type
