fred123123

could you make the website look more vibecoded please

That sound cool!, do you have hints on the cash prizes the website said something like 2 M ???

Hey! Any indication on what area of mathematics theses questions are from?

Classic God in a Box type argument, yes you can then argue anything

ok but why no inference time RL? also i think that it is reasoning exactly like us humans do. Indistinguishable

Yes maybe you could scheme it, like think of 10 things you could try, spawn subagents, try them, perhaps one would succseed and yes that think that worked could be placed in a memory system. But there is also value in things that dont work ect. So true continual learning would be really good. I am currently reasearching in mathematics and some things it cant get, i really have to think of them myself. But other things where it is easier to "build" from already existing knowledge works really well. I kinda wanne know how this will continue. It can do things that are "in distribution", but can it build its own new distribution and continue building. Mabye ill try this using a normal RAG system. I would concider it a succsess if it create a new "theory" like 100 pages genuinely new stuff, and then applies it to a problem. But my intuition is this will not work lol

The reason i am asking is about research mathematics. In this application what is really important is continual intuition and theory building, this takes time and trial and error. However this is very fruitful in solving problem. Currently the LLM can do mathematics that is somehow already in their weight very well. See the recent things. But i suppose true research would require continual learning that goes beyond the context window. Thats why i am wondering if this solvable. My intuition is that it is easily solvable, hence we could make a true mathematical continuously building theory to prove things.

I mean it gains the same level of expertise as it does on stuff in training data.

Yes i agree, but somehow knowledge got put into the LLMs head during train time, but why does it not work during inference time? Too little examples? Do llms know stuff with exactly one occurence in the training data?

How do you handle things like scrolling quickly in a video?