castform founder here. despite our bet on fine-tuned smaller open-source models, i'm still quite bullish on the big labs. i think scaled closed models will continue to dominate for more general purpose use-case like codegen, search, etc. but intelligence has lots of long-tail applications and i think for these longer-tail applications, finetuned custom models will rule
I would also like someone to clarify this for me. I (think) I understand what people mean by a neurosymbolic model however common definitions are a bit funky.
If it's neuro (llm/transformer similar) symbolic (symbolic with hand crafted rules) then, imo, that's no different from current tool calling harness implementations and I'd love for someone to explain it further to me if I've misunderstood.
If it's neuro (llm/transformer similar) symbolic (symbolic rules that have also been learned via training) then yeah I can understand how it's a distinct concept.
But every definition I've seen tells me that a standard harness of:
query->llm->[tool call symbol + tool name + tool params] aka symbols governed by logic, including param/arg validation->llm->etc
Already meets the requirements for being "neurosymbolic"...
