The Trinity: recursive self-improvement for multimodal agents via capability trees
Weidong Tang · hf · 2026-10-28
The Trinity framework from Cardinal AI Lab tackles two bottlenecks in multimodal agent self-improvement: costly construction of verifiable environments and manual harness iteration lagging behind task diversity.
Three roles: an Evolver reorganizes the task tree into a hierarchical capability tree based on shared capability requirements rather than surface categories; a Creator performs Capability-Driven Real2Sim2Real, abstracting common requirements of heterogeneous tasks at the same node into simplified, verifiable simulated worlds for training; an Explorer progressively evolves reusable capability modules along the tree. Specialized harness modules are then routed automatically, boosting performance across diverse benchmarks.
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