GoKawiilFrom our March 2025 arXiv paper on sequence conversion trajectories to Laya: a sub-35ms open-weight System 1 decision engine with RLCD, multilingual routing across 100+ languages, and state-of-the-art calibration.
Everyone in AI right now is talking about a new kind of model: an architecture that is not autoregressive, does not generate text, and gives lightning-fast probability predictions over structured schemas.
Seeing the hype online feels both validating and deeply frustrating.
I worked on this literally one year back in March 2025. I spent months of hard work, sweat, and sleepless nights building it, published an arXiv paper (arXiv:2503.23303), released the model weights on Hugging Face (sales-conversion-model-reinf-learning), published the open dataset (saas-sales-conversations), built a PyPI package, and posted the whole approach on Reddit (r/LocalLLaMA discussion).
Then in September 2025, I published a second paper (arXiv:2510.01237), formalizing the framework for schema-based decisions guided by reinforcement learning. The guiding brain in my system was always reinforcement learning, not just an embedding model or an autoregressive LLM.
And then in September 2026, a well-funded frontier lab called TypeSafe AI (founded by Diogo Almeida, a co-inventor of ChatGPT at OpenAI) launched Jev. They proposed the exact same non-autoregressive decision concept as if it was a brand-new scientific breakthrough. Except they launched without technical papers, without open weights, and with zero open training datasets.
My earlier model used PPO over sequence representations to output turn-by-turn conversion trajectories (probabilities from 0.0 to 1.0) in vertical sales conversations. Jev generalized parallel sampling using what they called RLCD (Reinforcement Learning for Calibrated Decisions) to output confidence distributions and schema choices horizontally, charging $0.042 per million input tokens with typical response times around 150 ms.
Instead of staying bitter, I decided to take everything I learned, fix every architectural limitation of the old approach, and build a completely open, horizontal System 1 decision model family: Laya.
And because we built it properly on bidirectional encoders, our models run in 32.8 milliseconds on a single GPU (7.2 ms/question batched), making it 6 to 8 times faster than Jev, with full support for over 100 languages, zero API subscription costs, and 100% open-source Apache 2.0 weights.
1. The Core Realization: System 1 vs System 2
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