Convai Innovations presents Laya, a multilingual, non-autoregressive system 1 decision model designed to provide typed answers with mathematically calibrated probabilities in a single forward pass. Unlike generative models, it does not generate text, thereby eliminating hallucinations and the need for parsing. The framework includes an automated Router that detects language and script to dispatch tasks to the most efficient checkpoint (English or Multilingual) within approximately 35ms on GPU.
- It is trained using Reinforcement Learning with Calibrated Decisions (RLCD) to ensure honest probability reporting.
- Laya can support context lengths of up to 8,192 tokens in its multilingual version.
- The model family includes specialized checkpoints like `laya-typed-decisions` which achieves significantly higher accuracy through fine-tuning on specific workflows.
- Performance benchmarks show it is roughly 6–8× faster than TypeSafe Jev for single question latency on a T4 GPU.
- It is trained using Reinforcement Learning with Calibrated Decisions (RLCD) to ensure honest probability reporting.
- Laya can support context lengths of up to 8,192 tokens in its multilingual version.
- The model family includes specialized checkpoints like `laya-typed-decisions` which achieves significantly higher accuracy through fine-tuning on specific workflows.
- Performance benchmarks show it is roughly 6–8× faster than TypeSafe Jev for single question latency on a T4 GPU.
yibie maintains a curated awesome list of over 300 public projects built on Jev, TypeSafe AI's System One model that takes unstructured state plus a typed question and returns a typed decision (Choice, Score, or Noul) with a confidence value in a single forward pass. The list spans 14 categories from classification and routing to agent safety guardrails, database extensions, and open-source replicas, enforcing inclusion rules that require public, citable sources demonstrating a genuine typed-decision use of Jev.
- The repo explicitly warns that same-day bulk submissions from one author sharing a scaffold can satisfy every inclusion rule while remaining unproven, and treats volume as not evidence of quality
- Open replicas range from 547k parameters (minojev) to 395M (von); one 706K-parameter model beats Jev at form-filling (99.7% vs 83.6%)
- Jev performs no token generation; its speed comes from parallel constrained decoding, so an inference engine can expose a Jev-like API over any open-weight model
- A CAPTCHA arbitrage example illustrates per-decision pricing: solving at $0.0068 per hundred against a marketplace paying a cent each
- LangChain published both a Jev harness wiring guide and an independent evaluation concluding Jev is the cheaper and more consistent judge for online evals versus LLM judges
- The repo explicitly warns that same-day bulk submissions from one author sharing a scaffold can satisfy every inclusion rule while remaining unproven, and treats volume as not evidence of quality
- Open replicas range from 547k parameters (minojev) to 395M (von); one 706K-parameter model beats Jev at form-filling (99.7% vs 83.6%)
- Jev performs no token generation; its speed comes from parallel constrained decoding, so an inference engine can expose a Jev-like API over any open-weight model
- A CAPTCHA arbitrage example illustrates per-decision pricing: solving at $0.0068 per hundred against a marketplace paying a cent each
- LangChain published both a Jev harness wiring guide and an independent evaluation concluding Jev is the cheaper and more consistent judge for online evals versus LLM judges
