TRINITY: An Evolved LLM Coordinator

Key points

The core idea is that a compact coordinator, a 0.6B LLM plus a 10,000-parameter lightweight head, assigns Thinker, Worker, and Verifier roles on each turn and delegates work across multiple LLMs.
Optimized with a separable covariance matrix adaptation evolution strategy (CMA-ES), it outperforms RL, imitation learning, and random search under high dimensionality and strict budget constraints.
It achieves state of the art on standard benchmarks, including 86.2% on LiveCodeBench, and consistently outperforms individual models and existing multi-agent methods across coding, math, reasoning, and domain knowledge.
The coordinator's hidden-state representation provides rich contextualization, and block-epsilon separability in the optimization landscape enables efficient evolutionary search.
It generalizes robustly to out-of-distribution tasks without task-specific tuning.

External research summaries. These are not HDATF publications or measured product results.