Trinity Framework Enables Recursive Self-Improvement for Multimodal Agents

Cardinal AI Lab's Trinity framework enables multimodal agents to recursively self-improve by reorganizing tasks along a capability tree with a three-role mechanism, addressing costly heterogeneous verification environments and slow manual harness iteration, achieving an average 34-point gain on training benchmarks.

2026-10-28 ~ 2026-10-29 · 2 related posts

• The Trinity: recursive self-improvement for multimodal agents via capability trees — Weidong Tang · 2026-10-28
• The Trinity paper: three-role recursive self-improvement lifts benchmarks by 34 points — mark_k · 2026-10-29