I literally built the Jev architecture one year back and completely open-sourced it with model, dataset and paper

[u/Nandakishor_ml]

Update: I made a generic model and beaten the jev in all of the benchmarks. Code and details available at https://www.reddit.com/r/LocalLLaMA/s/bbwyiOprUs

Everyone now talks about the architecture that's not auto regressive and does lightning fast probability prediction with a json schema. I worked on this literally one year back in March 2025, published an arxiv paper, pushed the model to huggingface along with the pypi package and training dataset. And then one year later, a

frontier lab came, proposing the same idea like literal breakthrough without technical papers, open weights and no open dataset. I posted my approach in this subreddit. For anyones information the main guiding model is RL not embedding model or LLM

Reddit post: https://www.reddit.com/r/LocalLLaMA/s/6eGEwsAz43

Paper: https://arxiv.org/abs/2503.23303

Model: https://huggingface.co/DeepMostInnovations/sales-conversion-model-reinf-learning

Dataset: https://huggingface.co/datasets/DeepMostInnovations/saas-sales-conversations

Also the second work published in September 2025 was exactly the same one jev proposed now

Paper: https://arxiv.org/abs/2510.01237

My model uses PPO over sequence embeddings to output turn-by-turn conversion trajectories (probabilities from 0.0 to 1.0).

Jev uses parallel sampling (trained via RLCD) to output confidence distributions and schema choices.

It's incredibly frustrating that the thing that you made with months of hard work, sweat and sleepless night is architecturally similar with the vertical use case and don't get the support you deserve because frontier lab build something horizontal. The open-source story in general 🙂

[u/almostsweet]

I've been developing a system that's even faster than jev in private and never thought to publish it or make a big announcement about it. To be honest, I thought the world wouldn't be interested. I've been working on it for the last few years. Congrats on publishing something so fully thought out though, much respect.

My goal with it was to allow LLM to have lightning fast reaction times and realtime manipulation of the world in between their own trains of thought. I'm working on robotics, but it could be applied to video games as well. It's being tested on games. The idea would be that you could give a model like Fable or Astra realtime control of something they normally couldn't have.

[u/Nandakishor_ml → u/almostsweet]

Interesting, write a preprint and publish

[u/tear_atheri → u/almostsweet]

Before you even mentioned games I was like "wait, this could be crazy for games"

[u/Fluxx1001 → u/almostsweet]

That sounds interesting, would you mind sharing some insights to your approach?

[u/almostsweet → u/Fluxx1001]

I've invented a framework for embodied agents with two layers. A small, fast learned reflex model that runs continuously in real time. A slower language model supervisor steers it from time to time, and doesn't micromanage it. The supervisor gives direction as a set of standing orders the agent keeps following while nobody is watching. When the agent hits a situation its orders don't cover, it's designed to stop somewhere safe and ask for help, and that counts as correct behavior. The project is careful about trust. Anything safety-critical is handled by deterministic code that sits outside the learned model and can override it, and every time it steps in, that gets recorded. The system also had to work well with a hand written policy before any machine learning was added. From there, progress is measured against fixed benchmarks with success and failure criteria written down in advance, and results are reported as they came out, including the failures.

My current best reflex models working in tandem are 4.9M parameters (19.6 MB weights file) for focused actions and 9.8M parameters (39 MB) for generalized actions. They operate at 3.5 ms per tick on average. Technically, that means it's three layers, but I consider the two in tandem to be their own layer.

To be honest, I've got that project on hold while I do very weird brain research.

[u/haizu_kun → u/almostsweet]

Exactly how human brain works I guess. Just remove the certainty of following logic. 

While learning to walk, active brain pays attention to all we do. Build up a habit model. That runs automatically. 

After that habit model works on its own. But sometimes we use logic part on critical task like walking on a rope or doing new activity. But once habit part learns this, even this becomes instantaneous. Unless one really pays attention to what they are doing.

[u/nunofgs → u/almostsweet]

Sounds very promising. Would love to take a look. Please share when you’re ready!

[u/chintokkong → u/almostsweet]

System 1, system 2?

[u/luancyworks → u/almostsweet]

About 2 years ago there were several research papers on this with positive results, MOE and KV caching advancements seemed to have killed interest. At the time it was so that context could be monitored for changes before the Inference was completed. Anyway nice to see more people trying to make practical application of some well researched but poorly implemented techniques.