classification

spinf-serve is an open-source (Apache 2.0) inference server that reads content once and answers many calibrated classification/decision questions in a single GPU forward pass, returning probabilities over user-defined options via an OpenAI-compatible API.

A Voyage AI practitioner argues that embedding inputs with a cheap embedding model and training a simple sklearn classifier on labeled data beats hype-driven zero-shot decision models like Jev, reporting a 37% error reduction at lower cost (with the caveat that this advantage only applies when training labels are available).

该论文研究在RAG知识冲突分类任务中，使用typed decision models替代大语言模型的可行性，结果表明决策模型在4-39倍低成本和更低延迟下可匹配或超越Gemini 2.5 Flash的准确率。

Liquid AI released d1-2B, a 1.92B-parameter decision model for text, images, and video built on LFM3-VL-2B that returns calibrated typed answers in a single forward pass with no token generation, achieving millisecond-scale inference and leading open d1 benchmarks.

Arize Phoenix evals (TS) now supports running any classification evaluator on a decision model (e.g. gpt-6-luna via OpenAI's Decisions API), returning labels and probabilities per option with each call traced as a decision span.

Show HN: STROT，一个开源「决策编译器」，用 LLM 编译一次意图，之后在本地以 144M 小模型零成本执行分类、评分与路由决策，附带校准与置信度门控保证。

This paper introduces a geometric framework using the average kernel size to derive deterministic, model-independent bounds on empirical error and accuracy for regression and classification in metric-space settings, highlighting an accuracy–stability trade-off and an empirical error floor caused by data ambiguity.

The paper studies how users seek legal guidance in real-world LLM conversations, introducing a taxonomy, annotating 2,122 WildChat conversations, and releasing a 200-conversation evaluation set along with trained classifiers and public resources.

A research paper presenting an automated framework that classifies suspected overdose deaths from death investigation reports using expert-guided explanations, showing that a pipeline model with input-grounded rationales outperforms self-rationalizing models and a deployed rule-based system by 15%.

该论文研究了在表格数据上使用"类型化决策模型"（返回每个固定选项概率而非生成文本的语言模型），在行分类、事实验证和异常检测任务上与三个LLM对比：决策模型成本更低且在21种等义改写下更鲁棒，但所有模型对表格组织方式的改变都不鲁棒。

AgriJev 将 Qwen3.5 系列（0.8B–9B）适配为农业领域的候选评分模型，通过单次前向传播直接输出候选分数与相对概率；AgriJev-9B 在 17 个外部测试视图上达到 75.44% 平均准确率，且评分延迟约为同规模答案生成的一半。

shad0w is a tiny caching proxy that sits in front of any OpenAI-compatible local LLM server, learns the model's classification answers, and after ~1,000 samples compiles a table serving repeated queries in microseconds with 95%+ agreement guarantees. Apache-2.0, written in Python/JS/C, with a demo of it learning Snake from an LLM's moves.

作者发布 Jebadiah v2.1（27B 与 9B）开源模型，将决策建模为封闭集打分问题，直接从候选标签 logits 输出各选项概率，并公布在 Decision Index 0.3 上的自测成绩、污染检查与量化一致性结果。

作者提出一个用于区分文本和图像创作中 AI 使用程度的五级分类框架（Penned、Authored、Composed、Directed、Generated），以取代过于粗糙的"是/否使用AI"二元划分，帮助创作者透明地标注工作流程。

Microsoft 推出 Decision-1 决策打分模型，用于代理与工作流的路由、分类、验证和控制，单次推理即可输出各选项的校准概率。该模型基于 Qwen3.5-9B 后训练，据称中位延迟比 GPT-6 Sol 快约 35 倍，现已在 Microsoft Foundry 上线。

微软发布 Microsoft-Decision-1，一个专为快速决策打分设计的模型，可用于路由、分类、优先级排序与工作流控制，基于 Qwen3.5-9B 后训练，在 36 项基准测试中取得最高准确率，延迟比 GPT-6 Sol 快约 35 倍。

该研究评测了轻量级开源语言模型在资源受限的边缘环境中对智能数据模型(SDM)进行分类的性能，涵盖通用、推理专用和代码专用架构，并与TF-IDF及轻量句子编码器基线进行对比，为边缘部署的模型选择和任务设计提供参考。

OpenAI 的 Decisions API 进入公开测试版，可对文本、图像或两者进行评估并返回类型化答案，速度约为 Responses API 的 10 倍，目前仅支持 gpt-6-luna 模型，预计数周内正式发布。

LiquidAI released d1-3B, an open 3B-parameter multimodal decision model built on LFM2.5-VL-3B that answers typed questions over text/JSON/image states in a single forward pass with zero output tokens, achieving 8ms decisions on consumer GPUs.

This paper introduces a novel weighted association rule-based data mining technique for breast cancer classification, using Rule Generation, Rule Pruning, and Rule Prediction algorithms to create interpretable rules for medical professionals and evaluate their predictive performance.