3 ms·
Open-sourced jev architecture last year with model,paper and dataset
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. For anyones information the main guiding model is RL not embedding model or LLM
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
- nandakishor_ml 19d agoFeeling that open-source community do not get the recognition they deserve
- user547 18d agohttps://github.com/vllm-project/vllm/pull/57250 https://github.com/vllm-project/vllm/pull/57250
- creamyhorror 19d agoThe world's heavily about marketing, resources, connections, and signaling, unfortunately. You probably needed to market it in a bigger forum with shinier claims to attract attention (I don't think a paper on Arxiv is enough).
- gcgbarbosa 19d agoPut it on your CV "I can come up with revolutionary ideas one year earlier than entire billion dollar organizations" "I inventet Jev a year earlier"
- rgon 18d agoterrible, antisocial and wrong take
- 37sysma 19d agoPity because without the frustration it would’ve been a good post. Catch is you’ve got intuition, but looking around instead of forward. Do it again, open-source it, either you’d quietly bring down few companies, or, when you’re close, you’d get offers from them. The who’s done what is a dying paradigm.
- nandakishor_ml 19d agoWe are human , frustration is something we feel when same kinda architecture is closed sourced and we celebrate it
- bigbadfeline 18d ago> We are human , frustration is something we feel Absolutely. Besides, when we see injustice we shouldn't be silent about it because that would encourage more of it. > when same kinda architecture is closed sourced and we celebrate it I saw the post about Jev and the lack of basic information felt wrong and bizarre, I now see the most likely reason for it. The best thing to do now, is to develop your open source project further and beat the Jev-ers, that story is very motivational and I'm interested.
- deleted 15d ago[deleted]
- azterizm 15d ago> open source project further and beat the Jev-ers That is a seriously good idea. I tested Jev against local models. Confirmed that local architecture outperforms the closed cloud one marginally. Limits mostly come from the cloud model. We are talking 712ms P50 latency versus 0.14ms locally on intent routing. Scoring costs linearly with volume. Cloud got its own limits. An open source Jev would resolve all this immediately. Network, billing, RPM ceiling, all resolved. Additionally, we can do fine tuning, scale with hardware and freeze weights.
- tmpsvc2695f5 19d ago[dead]
- saeranv 18d agoCan you (or someone) provide some context for this post? What's Jev? What's the innovation here that was duplicated by the frontier lab?
- deleted 18d ago[deleted]
- aesthesia 17d agoFrom what I can tell these are classifiers trained for a single task. What excites people about Jev is that it can do zero-shot structured responses for arbitrary prompts. Now, this isn't new either; models like GLiNER have been around for a while. But Jev appears significantly more flexible and polished while still being cheap and fast.
- gokuljs 17d agoSuch interesting comments. I’d never find this kind of discussion on Twitter. It makes me think
- tomrod 17d ago> 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. Totally get the frustration. Don't get too bent up about it -- take it as the market validating your hypothesis in a way you didn't expect. You should feel proud! That skillset - finding niches that could be humongous given the right cultivation, luck, funding, and marketing - is amazing. I feel strongly that, if JEV and/or your model prove valuable as many of us are already thinking and hypothesizing, people will come knocking. While LLMs are a neat space, this is a desperately missing component, and as someone who has built conditional choice probability models for almost two decades, I'm wildly interested to dig in this weekend and review the usefulness, the applicability as a homo economicus level of automation in a noisy prompt space (e.g. ensuring transitivity and IIA), and looking at this as a strong evolution. I've added your model to my eval list!
- mjb20 17d agoLol no one cares, make a good product of it
- fbi020 15d agoJust like the algorithm for short videos, even if you implement its prototype, it is hard to attract attention when you lack marketing and do not have an out-of-the-box product (you have the model weights, yet few people will deploy them). It will not become an instant hit like the Jev model; even others' secondary development may be more popular than your prototype.