Evolutionary merging with a 14-dimensional genome and MRI-Trust Fusion produces models that outperform their trained parents on reasoning benchmarks without any gradient updates.
Evolutionary optimization of model merging recipes
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OMEGA framework generates novel ML classifiers via meta-prompts and executable code that outperform scikit-learn baselines on 20 benchmark datasets.
HeteroFusion fuses heterogeneous LLMs via topology-based alignment and conflict-aware denoising, outperforming merging and ensemble baselines in cross-family and multi-source settings.
Tunable MAGMAX adds a tunable preference vector to model merging for continual learning, enabling automatic adaptation to target environments using small amounts of data while maintaining or improving task-wise performance.
Data flow space model merging is formalized as a mixed binary-continuous black-box optimization problem, where a structured approach respecting variable dependencies achieves 6.7% higher accuracy and 51.4% smaller search space than unstructured methods on real language models.
citing papers explorer
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Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning
Evolutionary merging with a 14-dimensional genome and MRI-Trust Fusion produces models that outperform their trained parents on reasoning benchmarks without any gradient updates.
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OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms
OMEGA framework generates novel ML classifiers via meta-prompts and executable code that outperform scikit-learn baselines on 20 benchmark datasets.
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Can Heterogeneous Language Models Be Fused?
HeteroFusion fuses heterogeneous LLMs via topology-based alignment and conflict-aware denoising, outperforming merging and ensemble baselines in cross-family and multi-source settings.
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Tunable MAGMAX: Preference-Aware Model Merging for Continual Learning
Tunable MAGMAX adds a tunable preference vector to model merging for continual learning, enabling automatic adaptation to target environments using small amounts of data while maintaining or improving task-wise performance.
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Black-Box Optimization of Mixed Binary-Continuous Variables: Challenges and Opportunities in Evolutionary Model Merging
Data flow space model merging is formalized as a mixed binary-continuous black-box optimization problem, where a structured approach respecting variable dependencies achieves 6.7% higher accuracy and 51.4% smaller search space than unstructured methods on real language models.