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A personal project describes a village simulation where a 144M-param julia-1 'gut' model scores villagers' actions on CPU, embeddingGemma retrieves relevant memories, and Gemma 4 e4b (6GB laptop GPU) is only invoked when plans fail or surprises occur. Results show the gut is rarely uncertain, and nightly rule-learning from mistakes cut injuries from 2.73 to 0.53 per valley.
A curated list of tiny-to-medium models (mostly under 35B) suited for running LLM agents on a normal computer without a high-end GPU, ordered by capability and highlighting models with vision support.
A user asks whether Gemma 4 E4B remains the best small multimodal model (under 8B/6GB) for running a local, tool-calling personal assistant on a Mac via MLX, noting failures with Ling 3.0 tiny, LFM models, and Qwen 3.5 4B/9B.
Yann LeCun and Randall Balestriero mathematically prove the Platonic Representation Hypothesis and show that JEPA training with scene-related positive views achieves image-text alignment with 42× less data, with a 109M-parameter text JEPA outperforming LLMs up to 72B on cross-modal benchmarks.
In a 16-minute conversation, Karpathy, formerly of OpenAI and former head of AI at Tesla, said that agents are not magic but the result of large-scale distillation. He advocates building a truly effective agent stack using small models paired with the right tools and closed-loop feedback, rather than relentlessly pursuing larger models.
Microsoft releases Mage-VL-Cowork, a compact 4B vision-language model that combines GUI interaction and terminal/code execution in a single policy, achieving 66.0% on OSWorld-Verified — the best result at the 4B scale — via a five-stage curriculum and online RL.
研究训练小规模GPT-2模型于儿童日常语音转录（BabyView）和合成儿童导向对话（TinyDialogues）数据上，发现模型会显著特化于特定儿童的语言环境，多儿童数据能提升对混合留出语言的预测但对语法基准改进有限，表明小数据语言学习不仅取决于数据量，还取决于环境来源与匹配度。
A successor to the Tesseract OCR engine has been released as indra by datalab-to, notable for being just 1M parameters while showing strong gains in both quality and speed on benchmarks.
实验探索用 DeepSeek-V4.1-Flash 作为协调者指挥 4 个 Qwen3.5-9B 本地小模型对二进制程序进行逆向工程，结果显示小模型协作在小任务上纯属开销，但协调者动态改写工人提示和提供工具是最有趣的发现。
A practitioner ran 10 agent harnesses on Qwen3.5-4B across 5 tasks and found that failures came from exact-match edits, literal instruction-following, miscounting, and API mismatches — not malformed tool calls. Six of ten agents passed all tasks, with an 11x spread in run time on identical hardware.
The author proposes replacing large 'know-it-all' AI models with small models that navigate externally stored knowledge 'maps' built by a retrieval agent, trading memory for storage while keeping results repeatable. The post weighs pros and cons of the concept and invites discussion.
A benchmark study on Terminal-Bench Lite finds that a bare IPython harness can match heavier production agent harnesses for Qwen3.5-9B, and that much of the run-to-run variance in small-model agents stems from noisy tool outputs rather than the model itself.
推文分享了一套实用配方：找到可重复任务，用任意模型完成并记录运行结果，然后微调最小的开源权重模型（如 FastConformer）达到目标精度，从而以极小算力获得接近 SOTA 的速度与性能。作者以自己用 Parakeet 采集数据训练 FastConformer 的实际经验为例。
普林斯顿大学与Google的联合论文提出JevAgentHarness，将智能体工作流中的常规决策（如工具选择）交给经智能体实际状态训练的小型决策模型（如AgentDecision-4B），在保持成功率的同时使成本降低81%–89%、速度提升最高2.5倍。
开发者介绍了 amber-8，一个将 80 年代汇编式确定性代码与本地小决策模型的概率判断相结合的编程实验，用于把自然语言指令拆解为可执行的栈操作代码。
中国研究者发表题为《The End of Harness Engineering》的论文，主张围绕模型编写的程序性 harness 应由自动化循环自动生成与评分迭代，人类只需负责编写评测集，而大模型本身保持不变。
Maxime Labonne highlights that small decision models like d1-omni-600M can be extremely effective when fine-tuned, with Leonie sharing notebooks on fine-tuning the d1-3B decision model using Unsloth and Hugging Face TRL in 30 minutes on a free-tier GPU.
A 0.6B parameter d3-edge model, fine-tuned with SFT + LoRA on a few thousand examples, outperformed the 27B d3 decision model on typed decisions and nearly matched it on Banking77, highlighting the value of fine-tuning small specialized models over large general-purpose ones.
Over the past few weeks, overseas teams have frequently post-trained GLM-5.3-Flash for vertical domains, achieving SOTA results — including glm-legal for legal, Ultramarine for knowledge work, apex-flash-1 for cybersecurity, and Datadog's Scout 1.0 for production incident troubleshooting.
文章介绍了海外小型AI实验室Paper Instruments（YC S25批次，融资约400万美元），其专注于大模型后训练与非可验证领域的agentic RL，发布了基于GLM-5.3 Flash后训练的Ultramarine 1 Flash模型，宣称在知识工作任务上接近Claude Fable 5的性能但成本低约60倍，后训练总成本约30万美元，采用开源加推理API的商业模式。