Ornith-1.0: Self-scaffolding LLMs for agentic coding

(deep-reinforce.com)

66 points | by kordlessagain 1 day ago

3 comments

  • SwellJoe 1 day ago
    I added this to a benchmark I've been doing of how well agents find security bugs, specifically security bugs originally found by Mythos. It performs poorly with only read/grep/ls tools, but in a follow-up test with a full shell and Python, it doubled its findings (still a poor showing, but it does at least indicate it is doing what it says on the tin: making tools to help it solve problems). It also did worse than Qwen AgentWorld, another recent post-train of Qwen 3.6 MoE intended for agentic use.

    https://swelljoe.com/post/will-it-mythos/

    • hedgehog 5 hours ago
      It would be really interesting to see how the Qwen 3.6 35B model compares to the 27B on your benchmark.
    • kordlessagain 23 hours ago
      Good to know. Thanks for the research!
  • Balinares 16 hours ago
    I'd have expected this to get more HN attention. Qwen 3.6 35B capability in a 9B model is a bonkers claim.
    • juliangoldsmith 10 hours ago
      It looks like they're comparing Orinth 9B to Qwen 3.5 35B, not Qwen 3.6. I guess it kind of makes sense since it's a finetune of 3.5, but I totally missed until I looked closely.

      In my brief tests, Ornith 35B performed quite well. It won't replace DeepSeek V4 Flash for me, but if it was fast and cheap enough it might.

      I don't remember being super impressed with Ornith 9B, but I could see it being on par with Qwen 3.5 35B.

    • chid 15 hours ago
      I thought so too when I read the headline but I expect it's basically Qwen3.5-9B
  • nzach 16 hours ago
    Instead of training the model to directly answer questions we trained the model to always write and execute the code that would solve the question ?

    If that is the case, this isn't just a fancy way to perform prompt optimization?