classification

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.

Cloudflare introduces Clef and Clef-flash, open-source decision models hosted on Workers AI for high-speed classification and agentic workflows, alongside a new reinforcement learning platform that lets developers fine-tune decision models with their own data.

Nace AI released Drex 1.5, a decision-focused model under 10B parameters with a 128k context window, ranked #1 on the Decision Index 0.2.1 with a score of 58.28, priced at $0.04 per 1M input tokens.

This paper studies open-weight typed decision models (laya, von) built on Qwen2.5 and shows that their outputs are driven mostly by option labels rather than written definitions, an 'option-label bias' traced to prompt rendering rather than the constrained decision head. It introduces PolicyBench and a two-call test practitioners can use to detect the failure mode.

A self-run evaluation of two small confidence-scoring classification models — TypeSafe AI's hosted Jev and the open-source Laya — finds that label descriptions phrased around input features, consistent time windows, and calibration (ECE) matter more than model scale for downstream thresholding performance.

Koa-action is a Salesforce AI framework that formulates low-latency atomic decision tasks (classification, scoring, endpointing) as single-token constrained generation via atomic label tokens and supervised fine-tuning, achieving competitive accuracy with several-fold faster inference than frontier models like GPT-5 and Claude-4.8-Opus.

CADENCE 提出了一种置信度自适应的双专家网络，用于实现快速且准确的时间序列分类，是一篇 arXiv 上的学术预印本。

OpenAI announced the Decisions API in limited preview, powered by GPT-6 Luna, enabling apps to define questions and answer options for real-time content classification, request routing, and agent action selection.

The article evaluates the Jev decision model by testing it on real enterprise tasks at Glean, comparing it to alternatives like Kev and Laya, and discusses its practical trade-offs in flexibility, cost, and performance.

Jev is a new AI model optimized for fast and cheap decision-making in classification tasks, demonstrated with a hotel review classifier project using Apify and GPT-5-mini.

Julia 1 is a 144.3M-parameter AI model for classification, routing, and decision tasks, with benchmark results showing strong performance on clear choice tasks like AG News and Emotion but limitations in knowledge and reasoning.

这篇文章讨论了通过大量API测试揭示了AI模型Jev的底层架构，并批评了当前大型模型在分类、风险控制和标注方面的误用。

The article describes a Python wrapper for LLMs that uses token probabilities for classification tasks, extended to vision models with an example for real-time webcam frame analysis.

This paper introduces TAM-Chain, a multi-scale thyroid cytology classification method that employs absorbing Markov chains and Shannon entropy for uncertainty quantification, aiming to suppress false negatives and adapt to domain shifts.

OpenJudgement-4B-Preview is an experimental Qwen-based AI model fine-tuned for classification and scoring tasks, offering an open-weight alternative in early preview.

A hands-on tutorial from TogetherCompute team member @nutlope on fine-tuning a Qwen3.5 4B model into a Jev decision-making classifier for $17 in 25 minutes, with open-sourced model weights and code on GitHub.

Together AI has open-sourced a complete recipe, allowing you to fine-tune your own Jev classification model for just $17, and has released a new model based on Qwen3.5 4B.

Jev is a new AI model introduced by TypeSafe AI, categorized as a System One or Decision Model, that outputs floating-point numbers for classification tasks like spam detection and ranking with competitive pricing.

After analyzing over 600 builds using Jev, the author finds that the most interesting aspect is not flashy demos but the cost-effective automation of routine tasks, enabling large-scale economical AI applications.

The article describes integrating Codex with Jev, a new AI model from TypeSafe AI, to reduce token consumption by 90% by handling classification tasks quickly and cheaply, while Codex manages complex reasoning.