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发布 CalOn，一个 0.6B 参数的校准类型化决策模型，用于实体、关系与事件抽取，速度达每秒 120 句，是 GLiNER2 的 5.5 倍，并在 40 个基准上超越基线。权重、推理与训练代码均以 Apache 2.0 开源。
NandakishorM 开源了基于物理的类型化决策模型 Vega（800M 及 4B 参数版本），采用 Test-Time Training 引擎+适配器架构，支持 73k 上下文和图像输入，通过在隐藏层构建"结果山谷"并用带摩擦的小球物理模拟来判定输出意图，单次推理超越多项 JEV 基准。
webAI 发布了 TwIL LM3 Pro，一个基于 IBM Granite 4.2 3B、仅针对形式逻辑（如前提蕴含判断、规则归纳、Lean 证明批判）微调的 3.66B 模型，在逻辑复合基准上以一半以内的参数量追平 Qwen3-8B，并提供可在 CPU/4GB VRAM 上运行的 GGUF 量化版本。
Phocinae-Largha-150M-v1 是一个 144.3M 参数的决策模型（基于 mmBERT-small），接受结构化状态和类型化的是非/选择/评分问题，单次前向推理即可输出带置信度的校准答案，在自建英文评测集上取得 0.906 准确率，延迟低至 GPU fp16 21ms。
Musubi released PolicyLM-1.7B, an open-weights decision model designed to apply plain-English content policies in real time under 50 milliseconds, without retraining when policies change. The release rides the wave of interest in decision models sparked by Typesafe AI's Jev, with competing models from OpenAI and Amazon.
Clef Flash, a 9B model running at Q4 quantization on an RTX 5080, plays Google Snake in real time (within the game's 135ms turn limit) using only simple vision instructions, with no training or game-state hacking required.
NVIDIA 发布 Nemotron-3.5-Bolt-E4B-4GB，一款基于 Gemma-4-E4B-it 后训练的小语言模型（8B 总参数、4B 有效参数，Q4_K_M 量化），专为低内存、低首字延迟的本地语音助手与嵌入式边缘场景优化，采用 OpenMDW-1.1 开放许可。
An independent developer open-sourced Peacebell, a from-scratch small language model (291M and 148M parameters) specialized in World War II, trained with a custom pipeline on curated synthetic data derived from Wikipedia, alongside a private-question WWII benchmark, demo, and a customized vLLM fork.
Ai2（@allen_ai）发布开源模型 AstaBrief 8B，可将复杂研究问题与文献片段转化为带引用的报告，支持本地运行，开放权重与训练数据；@MaximeRivest 表示期待尝试小型专用 8B 模型的潜力。
Amazon introduces Strands Decider 2B, a small open-source 2B-parameter decision model that replaces a Qwen3.5-2B LLM torso's LM head with a pointer head for fast, low-latency classification and scoring in agentic workflows.
A personal release of Jeff-Qwen3.5-0.8B plus 9 task-specific LoRA adapters that acts as a fast, calibrated "System 1" classifier in front of larger models, claiming 38× faster decisions and +8.7 accuracy points over Qwen3.8-27B while using under 2 GB extra memory.
webAI released TwIL-LM3-Pro, a 3.66B open-source reasoning model that surpasses VibeThinker-3B, Qwen3.5-4B, and LFM2.5-8B-A1B on formal logic benchmarks and runs locally via llama.cpp in a ~2 GiB Q4 build.
TwIL-LM3-Pro, an open-source 3.66B-parameter model, scores 95.4% on BIG-Bench Hard's logic subset and is small enough to run locally on a laptop, showcasing the power of specialized post-training for task-focused small models.
Autodesk Research introduces FLOORA, a family of small domain-specific language models using a custom DSL for architectural layout generation, where a 0.6B model outperforms much larger frontier models with up to 92% VLM judge win rates on out-of-distribution buildings.
Xiaomi-OCR-0 is a unified 0.8B vision-language model for document parsing and OCR-centric understanding, built from Qwen3.5-0.8B-Base, achieving #1 on Real5-OmniDocBench and outperforming larger models like Qwen3.5-2B on OCR VQA benchmarks.
SelfJev is an open 4B decisions model that you can self-host, allowing typed questions (yes/no, pick one, pick any, score) over any text with calibrated probabilities, plus Jev-compatibility via TypeSafe's SDK and fine-tuning on your own data.
The author trained a small language model to replace Gemini Flash for a summarization task, achieving 97% accuracy with 0.06s latency, suitable for deployment in an internal app.
AgentJev-0.6B is a small open-source AI decision model that outperforms Laya on Typed Decisions with improved accuracy and significant computation reduction, enabling faster local inference.
The paper introduces NepLEGiT, a specialized small language model pre-trained from scratch on Nepali legal text to enhance legal knowledge accessibility and service delivery in Nepal.
This article details the release of NVIDIA's Nemotron-3-Nano-4B-GGUF model, a small language model designed for both reasoning and non-reasoning tasks with reasoning controllable via system prompts.