Trinity framework lifts multimodal agents 34 points on 28 tests
TL;DR
- The Trinity paper reports a 34-point mean performance gain across 28 training benchmarks for multimodal agents.
- On 18 unseen benchmarks the mean rises 26 points, with two real-world embodied tasks included.
- The framework reorganizes tasks by shared capability requirements rather than surface categories, using three roles called Creator, Explorer and Evolver.
The headline claim, from the arXiv abstract for The Trinity: evolved Harnesses "improve mean performance by 34 points across 28 training benchmarks and 26 points across 18 unseen benchmarks, including two real-world embodied tasks."
The framework runs three roles, Creator, Explorer and Evolver, which the authors say reorganize tasks "from surface categories to shared requirements," building what they call a hierarchical capability tree. Common requirements across heterogeneous tasks get abstracted into "simplified, verifiable simulated worlds for capability learning," under a procedure the paper names Capability-Driven Real2Sim2Real. A Capability-Aware Harness Router then picks module configurations from the tree.
The abstract names no individual benchmarks and does not identify the base model. The two embodied tasks are mentioned but not described. All gains are reported as aggregate means.
Originally reported by paper
Read the original article →Original headline: Trinity Framework Iterates Multimodal Agents From Mean Score 39 to 73 Across 28 Benchmarks via Capability-Driven Self-Improvement