by nullbio | 2025-11-25 01:19:54+00:00
Novel solutions require some combination of guided brute-force search over a knowledge-database/search-engine (NOT a search over the models weights and NOT using chain of thought), combined with adaptive goal creation and evaluation, and reflective contrast against internal "learned" knowledge. Not only that, but it also requires exploration of the lower-probability space, i.e. results lesser explored, otherwise you're always going to end up with the most common and likely answers. That means being able to quantify what is a "less-likely but more novel solution" to begin with, which is a problem in itself. Transformer architecture LLMs do not even come close to approaching AI in this way.

All the novel solutions humans create are a result of combining existing solutions (learned or researched in real-time), with subtle and lesser-explored avenues and variations that are yet to be tried, and then verifying the results and cementing that acquired knowledge for future application as a building block for more novel solutions, as well as building a memory of when and where they may next be applicable. Building up this tree, to eventually satisfy an end goal, and backtracking and reshaping that tree when a certain measure of confidence stray from successful goal evaluation is predicted.

This is clearly very computationally expensive. It is also very different to the statistical pattern repeaters we are currently using, especially considering that their entire premise works because the algorithm chooses the next most probable token which is a function of the frequency of which that token appears in the training data. In other words, the algorithm is designed explicitly NOT to yield novel results, but rather return the most likely result. Higher temperature results tend to reduce textual coherence rather than increase novelty, because token frequency is a literal proxy for textual coherence in coherent training samples, and there is no actual "understanding" happening, nor reflection of the probability results at this level.

I'm sure smart people have figured a lot of this out already - we have general theory and ideas to back this, look into AIXI for example, and I'm sure there is far newer work. But I imagine that any efficient solutions to this problem will permanently remain in the realm of being a computational and scaling nightmare. Plus adaptive goal creation and evaluation is a really really hard problem, especially if text is your only modality of "thinking". My guess would be that it would require the models to create simulations of physical systems in text-only format, to be able to evaluate them, which also means being able to translate vague descriptions of physical systems into text-based physics sims with the same degrees of freedom as the real world - or at least the target problem, and then also imagine ideal outcomes in that same translated system, and develop metrics of "progress" within this system, for the particular target goal. This is a requirement for the feedback loop of building the tree of exploration and validation. Very challenging. I think these big companies are going to chase their tails for the next 10 years trying to reach an ever elusive intelligence goal, before begrudgingly conceding that existing LLM architectures will not get them there.