There is so much opportunity for purpose built models like this. Ideally a harness should spin up a subagent to offload to targeted models for specific tasks like this. I know this is not a novel idea. Claude code does some of this by handing off the "explore" agent work to haiku. I just love seeing that specialized LLMs are being developed.
foota•21m ago
I feel like the future is people building applications with tightly integrated LLMs that work hand in hand with the application's own lifecycle and code.
I also didn't realize that people were using agentic harnesses for search, it's an interesting idea. If the context length is short enough it should be fairly cheap compared to running "normal" agentic coding workloads where you have O(100k) context length for doing almost anything.
Malp•14m ago
There are! Chroma has Context1, SID has SID-1, and you'd actually be surprised at how easy it is to post-train your own with pretty good pass@ recall@ ndcg@ etc.
There's also Hornet who have shared some interesting talks & blogs lately. I don't know that I'd exclusively use agents for retrieval the way Neon outlines here as well. I think distillation similar to what ZeroEntropy has done for bespoke retrieval & reranking with _some_ agent manipulation on top-k results works better (IME).
devolving-dev•4m ago
Models keep on improving though, so doesn't fine tuning become an ongoing task with ongoing maintenance burden?
ramon156•14m ago
Bit unrelated, I realized that z.ai gives you access to deepseek 4 flash. It's incredible how well it performs when given a detailed spec. I'm not sure I've seen a model one-shot like that, and I was already impressed by gemma 4's speed and efficiency.
aliljet•11m ago
There is a more serious question in here that's not being answered. How effective is the retrieval in finding buried needles in larger and larger haystacks. And there's a correlary question, how effective could you be in finding paired needles in that haystack where you need to hold a needle to unlock finding another needle.
richwater•5m ago
One thing that plagues [insert current FAANG] is the large amount of corpus knowledge that is outdated/misleading or just plain wrong. I'm curious how this addresses that if it's deriving the reward function from the corpus itself.
JCharante•3m ago
I have done my own testing and found that smaller models can beat their larger siblings on fact retrieval from documents. I haven’t investigated it in depth with a large enough dataset but my guess is that larger models overthink it while smaller ones just do it. I would like if they compared this with 5.6 Luna instead.
mrinterweb•22m ago
foota•21m ago
I also didn't realize that people were using agentic harnesses for search, it's an interesting idea. If the context length is short enough it should be fairly cheap compared to running "normal" agentic coding workloads where you have O(100k) context length for doing almost anything.
Malp•14m ago
There's also Hornet who have shared some interesting talks & blogs lately. I don't know that I'd exclusively use agents for retrieval the way Neon outlines here as well. I think distillation similar to what ZeroEntropy has done for bespoke retrieval & reranking with _some_ agent manipulation on top-k results works better (IME).
devolving-dev•4m ago