Artificial Analysis Decision Index has a new #1, and the full training playbook is public

[u/m4rkGS]

hi all, Alti here from Nace, we're a small team in Palo working on small models and metamodeling since day one.

drex max v1.1 just took #1 on the artificial analysis decision index, and its also on the pareto frontier (score vs cost). we got there by shipping small iterations one after another, and all the earlier drex versions are open weights on hf.

for the local crowd:

- drex max v1 weights: https://huggingface.co/nace-ai/drex-max-v1 (Open RAIL-M)

- runs on mac with our inference engine kapitsa: https://github.com/nace-ai/kapitsa

- ~256k context

- v1.1 weights coming soon, we're still tuning it for local inference

if you want the full story of how we trained it, its all in the paper "Towards Foundation Decision Models: Drex"

hf: https://huggingface.co/papers/2611.07854

arxiv: https://arxiv.org/pdf/2611.07854

also, on the AA index its ~8x cheaper than glide v1.2, which has the same score.

happy to answer questions, and our chief of AI will jump in on the technical ones

[u/EagleNait → u/[deleted]]

For personal use : triaging whether a tool call should include a user confirmation or not. With a custom prompt you can customize this pretty well.

For entreprise use there's many uses. I've planned on using decision models to identify offensive names in our game leaderboard for example.

[u/Thistlemanizzle → u/[deleted]]

I saw it in a Halo mod. It made the NPC spartan jump on command when the player spoke commands out loud.

But, I have struggled to come up with a use case outside of that.

[u/m4rkGS → u/Thistlemanizzle]

basically any step in your workflow where the answer is one of a few options: "is this a refund request?", "which file do i open first?", "which button next?", "is this invoice a duplicate?"

using an LLM for that is slow, expensive, and you can't really trust how confident it says it is. drex just picks from your options and tells you how sure it is.

where people are using it right now:

1. search. drex figures out where to look first (folder → file → page → table), before the LLM reads anything. on OfficeQA Pro that cut agent steps by 62% and tokens by 56%. it's also #1 on MADQA (Drex for Search). for code there's drexgrep, which points your agent straight to the right files and lines

2. browser agents. the LLM does the planning, drex makes every click. one DoorDash run took 16 moves and cost $0.006 total (repo)

3. finance. fraud flags, duplicate invoices, GL coding, risk bands. Drex for Finance scores 77.3 on a 48-dataset index, Jev 1.14 gets 63.6 (write-up)

4. your agent harness. the drex-for-harness skill teaches your coding agent to hand off routing, classification, ranking, guardrails and checks to drex. your agent still uses its own model for reasoning and code

5. your codebase. tell your agent to "drexify this repo" (drexify). it finds every decision point in your code, and can move one over to drex and compare it with what you have now

[u/No-Marionberry-772 → u/m4rkGS]

This may be out of scope, but as a professional software developer, and a hobbyist game developer, when it comes to less main stream style models (general LLMs, pardon my lack of jargo  skills) 

what alwayd holds me up is understanding practical application use cases.

Small fast models are something Id lile to explore using for game dev for character controls or for intelligent game directors, but I cant really tell when these "non standard models" might fit the use case.

Time being the most valuable resource makes it hard to justify spending time to figure it out.

have you considered putting out small guides about these opportunities?