XSLT removal will break multiple government and regulatory sites (github.com)

Essentially, instead of modifying the prompt itself, the system intelligently directs the prompt to the LLM that is best suited to handle it based on its learned performance and efficiency characteristics for similar types of queries. It's externally optimizing people's prompts.

hodgehog11 · 2h ago

Wow, that was fast.

I've thought for a while that ensembling approaches would become the next stage of LLM development after CoT, since it provides yet another effective, independent axis for scaling laws. Great to see that perspective is taking off. The open weight community has an opportunity to take these ideas and run with them better than OpenAI has.

bachittle · 2h ago

I’m fascinated by this new paradigm. We’ve more or less perfected Mixture-of-Experts inside a single model, where routing happens between subnetworks. What GPT-5 auto (and this paper) are doing is a step further: “LLM routing” across multiple distinct models. It’s still rough right now, but it feels inevitable that this will get much better over time.

phi-go · 1h ago

Does this have a compute benefit or could one use different specialized LLM architectures / models for the subnetworks?

NitpickLawyer · 2h ago

> It’s still rough right now, but it feels inevitable that this will get much better over time.

Yeah, the signals they get will improve things over time. You can do a lot of heavy lifting with embedding models nowadays, get "satisfaction" signals from chats, and adjust your router based on those. It will be weird at first, some people will complain, but at the end of the day, you don't need imo-gold levels of thinking to write a fitness plan that most likely the user won't even follow :)

Signal gathering is likely the driver of most of the subsidised model offerings we see today.

CuriouslyC · 1h ago

I mean, agentic workflows have been a thing for a while now, this is just agentic chat.

whistle650 · 1h ago

It seems they use 70% of the benchmark query-answer pairs to cluster and determine which models work best for each cluster (by sending all queries to all models and looking at responses vs ground truth answers). Then they route the remaining 30% "test" set queries according to those prior determinations. It doesn't seem surprising that this approach would give you Pareto efficiency on those benchmarks.

visarga · 24m ago

It's ok if you can update the router over time, the more data you have the better.

mgreg · 1h ago

Link to repo for those interested: https://github.com/ZhangYiqun018/AvengersPro

srekhi · 1h ago

Isn't this what NotDiamond (founded 2 years ago!) has been working to solve for? Maybe someone from their team will chime in (cc @t5-notdiamond)

biggestfan · 1h ago

Between these kinds of optimizations, improved data center efficiency, and smaller models being more capable, I wonder how long it will be before someone manages to make a profitable AI business. Maybe when they race to train better models slows down and they don't need to constantly upgrade capacity.

Justsignedup · 1h ago

Reminds me of the early days of cloud computing. It was very pricey, but once the tools caught up in 5 or so years, it went from "omg cloud is so expensive" to "omg cloud is only expensive when its worth building your own data center"

darth_avocado · 1h ago

AGI will not be a single model. It will be an ensemble of models that interact with each other. Just like different parts of your brain.

datadrivenangel · 2h ago

Paper and repo do not mention routing latency, which I think is a concern.

Also the paper has some pie chart crimes on page 6.

NitpickLawyer · 1h ago

Just from a brief look at the repo they seem to be doing semantic embeddings w/ Qwen3-Embedding-8B, which should be in the high thousands pp t/s on recent hardware. With a sufficiently large dataset after using it for a while you could probably fine-tune a smaller model as well (4B and 0.6B available from the same family)

cubefox · 1h ago

Based on my experience, the GPT-5 router either isn't very smart or is deliberately configured to be very stingy. It basically never uses the reasoning model by itself, even if that means it hallucinates nonsense.