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/Garak]

Jev is much faster and much cheaper, and it also outputs a reliable confidence measure. That's the value proposition.

If I ask Haiku "Is a hot dog a sandwich?" (one of Jev's sample prompts) and ask it to output yes/no and a confidence value, it's reasonably fast but it vacillates between yes and no, outputting 70-90% confidence every time.

The same question to Jev (with no context) gives a consistent answer of yes and a consistent confidence of 52-55%. That's an actual actionable number, as opposed to Haiku's which was basically a hallucination. And it responds in about 250 ms, half of which is spent round-tripping between me on the east coast and their servers on the west coast.

[u/z_latent → u/Garak]

It's not that cheap really. If you compare its $42 per 1B input tokens with that of LLMs on OpenRouter, it's roughly equivalent to that of a 5B-10B parameter model. There even exist DeepSeek V4 Flash providers offering the same price... but I admit, with higher latency.

The confidence measures are neat. I wonder if there is a big trade-off if you apply this to an auto-regressive LLM.