jacklondon

I found this paper quite interesting.

Instead of discussing “reasoning” in a vague way, it studies LLM behavior on 3-SAT and especially near the phase transition, where the instances become much harder. This brings the discussion closer to computational complexity and avoids bare benchmarking.

It seems to suggest that many models fail badly in the hard region, while some newer ones may capture a bit more genuine reasoning structure.

I wonder if this is a meaningful bridge between LLM evaluation and complexity theory, or if it is still mostly a stress test and not much more.

Very interesting. For me the key question is whether this kind of agent can generalize to real SAT application domains, not only benchmark instances. In problems like timetabling, encoding choices, auxiliary variables, and branching strategy can matter a lot. If it can help there too, this is a very meaningful direction.

Why not if it is to facilitate new discoveries and to extend the reach of computational tools to large swaths of clever and productive people?

I’m curious how this compares to traditional tools (like scripting in Python/R) for analyzing such datasets, both in ease of use and performance. Also, could similar query languages be developed for other fields (genomics, imaging, etc.) to empower domain experts? It’s cool to see a new DSL in academia

I wonder if there is any chance the system to be also installed in Eastern Mediterranean (Greece, Italy, Turkey) which is severely affected by earthquakes all of the time?

Fresh addition to the (semi-)gambling world of crypto and/or financial trading. It is interesting that it is regulated and thus it is not officially considered as "gambling" anymore.

It allows registration to US residents only. Anyone knows of anything similar in the European legal space?

I found that book but haven't read it. From the description though, it appears that it forms the base material for the course.

https://www.amazon.com/gp/product/B07FLQHLTK