Ornith-1.5
AssessPlatforms
A family of self-improving foundation models with MoE and dense variants.
Why it's here
Placed in Assess: 1 article(s) of evidence from 1 source(s), led by research breakthroughs, with 0 in the last 30 days. Confidence 24%. Low accumulated evidence, so it defaults conservatively pending more signal.
Evidence (1)
- 8Hacker News·8/19/2026breakthroughOrnith-1.5 advances self-improving open models
Ornith-1.5 is a new family of open models built around an end-to-end self-improvement loop that generates its own tasks, scaffolds, and solution rollouts for reinforcement learning. The release spans 397B MoE, 35B MoE, and 9B dense variants, with reported benchmark results that match or exceed several larger open-source and proprietary models on reasoning, coding, and agentic tasks.