I’ve been trying to leverage the current models in a way that bypasses the current “meta” of models making everything in “1 turn”.
I think that models kinda need to have “time sensitivity” but currently everything a model knows is just one single “snapshot” of the problem you give it, and then it tries to infer everything from that, and even the “reasoning” models, they perform the whole reasoning before trying to tackle the problem, and have no idea of what itself outputted.
What i want to explore now, is models that “reflect on it’s own outputs” so it does not only “reasons about the problem at hand” but also “reasons about it’s own output”. So I’m trying to create an architecture that will “Frankenstein” some Gemini models together to break down a problem, work on it, evaluate the result, and refine it if necessary before sending an answer to the user.
Of course, i’m here deep in this path now, and i need some external feedback, or even know if smarter people are already implementing this in a smarter way. Or just call attention to this problem so that the smarter people start to look into it and put it to good-er use! here i’ll send the link for my Google AI Studio project that i vibe-coded with Gemini to try to prove it. But yeah, the free limits requests are too short for me to gather enough feedback and tests on it. So if anyone is interested in trying it out or doing some spin on it feel free! it still rough around the edges but i guess 50% of the times it works 100% of the times!
I hope this works, if the link doesn’t work please tell me i’ll try to share it in another way if it doesn’t!
Also here’s the “deep research” i gemini used to create the overall approach to this.
Also see if the doc opens normally too.
My personal rambling about the motivation behind this project
So overall, that’s something that has been bugging me for a long time, ever since Gemini had access to the “double check response” using google search button built in, but it was something the user has to click manually, and if it does find a inconsistency, it does not prompt the AI to correct itself, this always made me question the whole point of the AI being used to search information, if it is not verifying it’s own outputs automatically and trying to guarantee that it’s information is up to date, or at least grounded in some source. And the shocking thing is that the current models are kinda capable of evaluating a corpus of text for fact checking, aren’t they? So why is this not a feature already? So that’s why I’m trying to explore this front of the problem. The fact that the current chatbot AI’s are all just a single-turn machine that have no idea of what it just spilled out, even though if they knew it, they probably would be able to self correct.
At least that’s my theory! An AI theory! that’s why i want to investigate it, and try it out! And also see if this approach I’m doing is anything valid, or if I’m just wasting TPUs for nothing! Also, if possible, and you are looking into it too, and have better ideas, or a better project with the same goal, lemme know! i’m very curious about that front! And i’m wondering why nobody seems to be pursuing it! That looked like such a “low hanging fruit” from the beginning specially for google who literally owns… well, Google!
