I want to clarify something. Reranker is a general term that can refer to any model used for reranking. It is independent of implementation.
What you refer to
because reranker models look at the two pieces of content simultaneously and can be fine tuned to the domain in question. They shouldn't be used for the initial retrieval because the evaluation time is O(n²) as each combination of input
Is a specific implementation known as CrossEncoder that is common for reranking models but not retrieval ones for the reasons you described. But you can also use any other architecture
Link to bluesky https://bsky.app/profile/tomaarsen.com/post/3llc2jvwah22f
Some more details https://huggingface.co/blog/train-reranker
Claude frequently draws svgs to illustrate things for me (I'm guessing it's in the prompt) but even though it's better at it than all the other models, it still kinda sucks. It's just fudamentally dumb task to do for a purely language model, similar to the arc-agi benchmark , just makes more sense for a vision model and trying to get an llm to do is a waste
Such dumbasses, even if this was a good strategy, they're still banning one company and let others (arguably more dangerous ones) go scot free