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The paper proposes a heterogeneity-driven framework for selecting complementary LLM teams by profiling error decorrelation and predictive divergence, then greedily optimizing a quality–complementarity objective, showing gains over quality-only top-k baselines across multiple benchmarks.
This paper proposes Frontier Learning, a framework that combines representations and predictions from multiple black-box and white-box pretrained models to construct a unified target-domain representation, guaranteeing performance no worse than any individual reuse baseline under distribution shift. Evaluations on visual domain adaptation and clinical mortality prediction show consistent gains over strong baselines.
Maestro is a reinforcement learning-driven framework that dynamically composes ensembles of frozen expert models and skills for multimodal tasks, achieving 70.1% average accuracy with a 4B orchestrator, surpassing GPT-5 and Gemini-2.5-Pro.