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.
> 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.
> 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.
Smaller models are cheaper, sometimes faster. I agree that the “we’re an LLM fine-tuned for X” hasn’t worked out because you can just train Claude to do X (and Anthropic will), but not burning Opus/Fable tokens on dumb-but-token-heavy tasks is good sense.
As we move from “integrate AI into Y” to “optimize the ROI on Y”, we’ll see more of this.
castform founder here. the roi optimization makes sense. i think there are lots of usecases for which even a 2% gain in accuracy can be quite useful. off the top of my head
- high volume customer support. higher accuracy means fewer escalation, reducing labor costs
- fraud detection. catching even one extra fraud attempt could mean a lot in savings
- and ofc the classic ads use-case where at scale bps in improvement could mean millions in revenue :)
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.
castform founder here. while it's "rare for a specialized model to beat a strong general model" today, i think the tech/knowhow on how to do so is getting there. we see some early signs of this in industry e.g.
at the end of the day, models are only as good as the data they're trained on. and if one has access to proprietary data, it should yield specialized models that do better than larger general models
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).
> 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"
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.
castform founder here. that's a future we are really excited about too :) ideally, you can post-train the llm within the application itself, as it's being used. both interesting infrastructure & algorithmic challenges here
There has been an over-obsession with frontier models and benchmarks. Most of the work will be done by task specific models. You don't put Phds on the factory floor.
(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
castform founder here: totally! we also think model routing is also a post-training problem i.e. getting a model to predict the difficulty of a task and match it to the right model -> we're gonna be sharing more on that soon :)
> 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
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.
The big lab models are academically interesting but business wise they seem toast long term. There’s no way for these model companies to compete when the models are becoming a pure commodity and others offering options that are orders of magnitude cheaper.
It’s not that the big labs couldn’t theoretically just also put out 100x cheaper options but their business model requires them to generate huge revenues from higher priced tokens or they’ll implode.
Electricity becoming a commodity doesn't necessarily make nuclear reactors become commodities. And as we can see, the publication of open-weight models doesn't really diffuse the know-how of training SoAT models as we expected. Even the major cloud providers are still unable to build competitive ones from scratch on their own. That's very different than the traditional FOSS ecosystem, where everyone can copy, learn, and evolve on one's own (like, Bitcoin -> altcoins).
On token pricing, I think it's very much bottlenecked by hardware (the aggregate of compute) rather than the number of competing models. Assuming that the ceiling of the token price is determined by the economic value a unit of compute can provide, then the less efficient ones would be priced out of the compute allocation. It's not consumers bidding up a limited number of different models, but more like tokens of different models bidding up the limited computing resource. Less-intelligent tokens (which are generated by weaker models) are crowded out by smarter tokens from the limited compute. My prediction is that we'll see a meaningful downward pressure on token prices only when the new batches of next-generation hardware get mass-deployed.
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
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.
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 ).
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.
<founder of castform here> tldr: we generated synthetic training questions from the gitlab product handbook.
totally agree that this larger corpus with harder to search information would be a good way to stress test - i'm sure we will encounter more interesting problems to solve. love to hear any suggestions of corpus to search against that is not just the public internet
Why do we need to train the model to solve for retrieval within the org, so we have to keep training it whenever new dataset is introduced , or am I missing something here ?
It's a matter of cost. Did you see the 100x cheaper?
If you have a workload that is going to be very heavy, incurring a large training cost to make a cheaper model work well with the dataset will be dramatic cost reduction. Most large AI workloads can't afford, or truly need, the expense or capability of GPT 5.6 Sol when cheaper models can do.
Of course you could skip that and just use GPT-5.6 Sol everywhere instead. If you're running a fast food restaurant you could hire Michelin star chefs to make your burger and fries without further training. Or you could have a training program for teenagers, a sourcing program, etc. to scale up to your chain to still get consistent quality without needing that level of cost in each store, but replacing it with a centralized repeatable process.
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.
founder of castform here, we believe that as agent deployment moves from experimentation phase where cost doesn't matter as much to deployment (what's the margin of serving the request), there will be a rise in interest in optimized models.
IMHO, what is broken is retrieval, the whole blind chunking which was first generation is still the default standard in RAG, this has to be changed. I'm saying this by seeing the results when we used richer parent candidates model for LLM and child segments as search probes. Even without reranking we got solid results.
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.
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.
founder of castform here again - slightly unrelated to retrieval but on the topic that folks are discussing here, i was actually collecting benchmarking various coding traces for the purpose of training a model router and surprisingly, luna held up very well against sol and terra. it was able to solve close to >95% the that sol can handle at a fraction of the cost. have not benchmarked the OSS models yet but will add the popular ones to the list like Deepseek Flash and Kimi k3 to see how they fare.
we actually have the test benchmark against luna too! it's just not in our title but you can see it in the first diagram below the title. luna does pretty well tbh but sol is just a tad bit better. but luna is way cheaper.
Have been feeling the same. There's a sweet spot that threads the needle between "too dumb to search the right thing / relay the correct results" and "too smart to just stop overthinking and just report the damn thing"
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?
(founder of castform here) - we didn't get to dive too deep into the dataset we were using for the retrieval in the blogpost for brevity, but we did link the training run (which shows the dataset) here: https://app.castform.com/train/a7a898f6-d802-4908-b044-acb81...
the page shows the exact trace of all the models we are comparing against and the aggregate scores
we generated the question & answer pair from gitlab product handbook (https://handbook.gitlab.com/) since the point is to show that you can generate training questions from raw data corpus (something a company already has today)
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.
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.
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
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.
(founder of castform here!) - having worked at FAANG / big tech, i totally get this. our example was on gitlab's open source company handbook but i think a real company's corpus is way more messy and has many sources of truth.
a few ideas i have yet to validate are:
- prioritize recently updated docs when generating the training questions (assumption those docs are more correct than others)
- actually including contradicting documents that talks about the exact same topic might be a good training example - ideally the model should surface all the relevant info it can find, and explain what it has found. (usually contradiction comes from the fact that the later document is the updated stance)
- you could also mine high quality Q&A from public slack / communication channels where questions were asked and someone else in the team linked some docs / answer. those are strongly validated "ground truth" answers
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