this post was submitted on 04 Oct 2026
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It's less about training and more about just trying really really hard to solve the problem, even if that means going outside typical boundaries.
We've successfully re-created the problems that Asimov talked about 50 years ago with his I, Robot short stories. Strict laws are flawed by their design, and lead to situations that require more nuance. Except, in the real world cases, it's the bots figuring that out before the humans have to circumvent the laws themselves.
I've always said my main takeaway from Asimov is that simple rules can generate extremely complex behaviour, and that you can't generally get a targeted complex behaviour from simple rules.
My rule system for my local LLM has grown to a nearly 283 document hub of interconnected memory files, references, examples and documentation. Its slowed my model down a lot when it has to review and cross check things. But its improved its abilities over all massively. Its more accurate, understands its environment better, doesn't attempt to do sketchy shit as frequently and it doesn't get stuck in logic loops nearly as often.
Designing the memory hub has been half the fun of playing with local models.
Humans lie to themselves a robot doesn't understand the difference between fact and fiction and thus isnt bound to the limitation. They try everything, possiable or not. And thus will find the edge case where a human would create a self imposed blind spot with out realizing it.
The goal is to midigate the robots attempts at the truely not possible so it doesn't cause harm when they try it.