Original Summary

I did it again guys, I built another harness, this one is the best one yet. https://github.com/doctarock/Speck A lightweight persistent cognitive architecture designed to make small language models substantially more capable by moving as much cognition as practical out of the LLM and into deterministic, persistent, measurable runtime mechanisms. I took all the clockwork out of my cognitive architecture, but stopped short of emotions, arousal, etc, just the logic systems https://github.com/doctarock/Artificial-Cognitive-Architecture-Omega-Gen2 Then I applied it to my agentic framework https://github.com/doctarock/local-ai-home-assistant And she is a doozie, small models in this harness outperform models twice its size without in both speed and accuracy: Speck on a single shared qwen3:4b scoring 15/16, against 13/16 for naked qwen3:8b and in half the time . Speck did that with half the parameters, less memory and less time per case. It comes from the model-scale series ([docs/experiments/model-scale/README.md](vscode-webview://01bk2i9taah3glr79lstr6r6crb9gv4hbl8ahqvem5sb2v8j1qd0/docs/experiments/model-scale/README.md)): 8 cases × 2 runs, with the same sampling and the same 1,024-token reply cap for every subject. Subject Largest model Success Time per case Model memory Speck, shared qwen3:4b (phase 6, current setup) 4B 15/16 7.9 s 5.7 GB Speck, shared qwen3:4b (phase 3b) 4B 15/16 13 s 5.6 GB Speck, all qwen3:4b (phase 3) 4B 14/16 24 s 5.9 GB Naked qwen3:8b 8B 13/16 13 s 6.7 GB Naked qwen3:4b 4B 10/16 16 s 4.3 GB Go get it, and don't forget to drop a star on your way through.   submitted by   /u/Electronic-Space-736 [link]   [comments]

  • 情报分类：技术学习与提效
  • 分类依据：内容涉及技术、AI、软件工具或工程实践
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/10/6 08:43:39