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Cloudflare announces a preview of Clairic, the first System One-class extraction model, hosted on Workers AI as @cf/cloudflare/clairic with open-weights planned for release. Clairic extracts, computes, estimates, and forecasts typed values from unstructured inputs with higher accuracy, lower latency, and lower cost than traditional LLMs.
Meta AI introduces MetaEncoder, which fine-tunes a pre-trained Muse-Glimmer 30B decoder into an instruction-following decision-making encoder using unidirectional contrastive learning, evaluating it across 11 benchmarks and 190 tasks for multimodal closed-set and open-set decision making.
RAW.works explores Recursive Decision Models (RDMs), applying the harness architecture of Recursive Language Models to 'System One' decision models like Jev, with examples built in DSPy showing LLM-free recursive, divide-and-conquer context processing.
OpenAI announced a new 'Decisions API' at Dev Day that functions like TypeSafe AI's Jev — a fast, cheap, LLM-based classifier producing probabilities over predefined choices — aimed at monitoring and controlling the behavior of OpenAI's swarming AI agents.
This article demonstrates how to adapt the GLM-5.3-Flash LLM to function as a typed decision model similar to Jev, achieving comparable accuracy and speed while enabling decisions on images in a single forward pass.
Lev is a 4B open-source AI model based on Qwen, designed for efficient typed decision-making in a single forward pass with zero output tokens, achieving strong benchmarks for its size.
The article introduces the Contrastive Language Model (CLM), which is 9x faster than Jev for System One models, and provides a guide on using Jev in building custom AI harnesses with Pi SDK.
Jev is a generalist System One AI model designed for making focused, structured judgments with probabilistic outputs. This article provides a beginner's guide and introduces an interactive playground for experimentation.
The article tests an AI model called Jev from TypeSafe AI, designed for fast, structured decisions rather than generation, aiming to improve efficiency in AI workflows by separating decision-making from general-purpose LLM tasks.
The article discusses testing and evaluating TypeSafe's System One AI model in the context of the 2048 game.