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.
benjiro29 28 minutes ago [-]
> Claude code does some of this by handing off the "explore" agent work to haiku.
That is not handing off to a specialized model, its just handing off to a lighter and interior model (compared to the parent model). That by itself can create issues like the lighter model not capturing all the data that the parent needs.
The idea is that we get specialized models that are better then general purpose models. But its rare for a specialized model to beat a strong general model.
There is a reason why we hear less about this idea of smaller expert models, because large strong models to the tasks just as good.
And if the tasks is repetitive to the point that specialization is useful, you can get into a situation that your better off having a program written for that reputative nature, then delegating to other models. And then have the main strong model, deal with the (semi)cleaned up data.
BikiniPrince 6 minutes ago [-]
You can register models with mcp. I think it’s an expensive solution, but it is available in the framework. I use a light weight bus protocol that lets agents interact and pass short messages with pointers. It’s very efficient.
phainopepla2 53 minutes ago [-]
> Claude code does some of this by handing off the "explore" agent work to haiku
This is no longer necessarily true. As of 2.1.198 [0] (released July 1st): "The built-in Explore agent now inherits the main session’s model (capped at opus) instead of running on haiku"
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).
foota 2 hours 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.
devolving-dev 2 hours ago [-]
Models keep on improving though, so doesn't fine tuning become an ongoing task with ongoing maintenance burden?
kumama 2 hours ago [-]
(one of the blog post authors here) -> once you set up a finetuning pipeline, it's often trivial to rerun it on top of a new open weights model. so, it's orthogonal to base model improvements
Razengan 1 hours ago [-]
> There is so much opportunity for purpose built models like this.
OpenAI etc could themselves do this, and maybe they already do? Where the public-facing interface delegates to multiple little goblins behinds the scenes
mrinterweb 58 minutes ago [-]
Exactly. There could be a lot of value for inference companies to do this. Could save a lot of money being able to hand off highly repetitive known tasks to far smaller specialized models.
aliljet 2 hours 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.
BikiniPrince 2 minutes ago [-]
I use detailed project files. It has data regarding the project and subtasks as well as task status. It doesn’t depend on agent context and it’s managed to keep the agent on track. Feature creep with the new models is a very real issue. Capturing principles and how to reconcile tasks helps too. Even today it brought up a source of truth issue it had detected. There were multiple authorities born out of a patch and it used that principle to highlight and resolve the problem.
Foobar8568 38 minutes ago [-]
Considering the state of the field ( RAG/retrieval/evaluation) I have 0 trust in it, even more if it's closed source with bullshit claim like that.
Everything is vibe sloped to death, and dead after a few months to a couple of years (and not hard to be 100 cheaper than GPT-5.6 sol ... DS is basically free and I guess already 100 times cheaper or more, and here another slope ).
dev_l1x_be 19 minutes ago [-]
I am not sure about GPT-5.6. It usually 10x more verbose for no apparent reason than GPT-5.5. Maybe it is only me.
BedVibe_Studios 42 minutes ago [-]
This feels like the database equivalent of "use the right data structure." We've spent two years assuming the biggest general-purpose model should do everything. It makes more sense for retrieval, reranking, reasoning, and generation to each have their own optimized model if the routing cost is negligible.
JCharante 2 hours 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.
barake 2 hours ago [-]
Anecdotally, it feels like Opus, Fable, and Sol "get distracted" when you use them for writing code. Great at reasoning and coordination but they will go off on a tangent and refactor half the code base. I only use them for reasoning (of course) and coordinating subagents.
andrenotgiant 2 hours ago [-]
Any data or public links you can share? That surprises me
breadislove 1 hours ago [-]
On what do you guys test the model. Its very dubious that there is no common retrieval benchmark such as browsecomp plus or similar tested. And what metric do you report?
krm01 58 minutes ago [-]
Keeping track of any AI progress is becoming harder by the day, because there's ambiguity around common/clear/consistent benchmarks. Everything is constantly skewed into favourable directions.
2 hours ago [-]
richwater 2 hours 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.
ramon156 2 hours 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.
swiftcoder 2 hours ago [-]
Deepseek flash (especially after the recent update) has to be one of the most slept-on models. Price-performance is ridiculous, and its available on a number of cheap coding subscriptions
Rendered at 21:11:17 GMT+0000 (Coordinated Universal Time) with Vercel.
That is not handing off to a specialized model, its just handing off to a lighter and interior model (compared to the parent model). That by itself can create issues like the lighter model not capturing all the data that the parent needs.
The idea is that we get specialized models that are better then general purpose models. But its rare for a specialized model to beat a strong general model.
There is a reason why we hear less about this idea of smaller expert models, because large strong models to the tasks just as good.
And if the tasks is repetitive to the point that specialization is useful, you can get into a situation that your better off having a program written for that reputative nature, then delegating to other models. And then have the main strong model, deal with the (semi)cleaned up data.
This is no longer necessarily true. As of 2.1.198 [0] (released July 1st): "The built-in Explore agent now inherits the main session’s model (capped at opus) instead of running on haiku"
[0] https://code.claude.com/docs/en/changelog#2-1-198
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).
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.
OpenAI etc could themselves do this, and maybe they already do? Where the public-facing interface delegates to multiple little goblins behinds the scenes
Everything is vibe sloped to death, and dead after a few months to a couple of years (and not hard to be 100 cheaper than GPT-5.6 sol ... DS is basically free and I guess already 100 times cheaper or more, and here another slope ).