Evolutionary trees from LLM weights recover ground-truth training topologies and identify key datasets and layers through phenotypic analysis.
Unsupervised model tree heritage recovery
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
MVProbe is a multi-perspective probing framework for weight-space learning that combines first-order and Gram-based views and outperforms ProbeX on the Model Jungle benchmark.
citing papers explorer
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Analysis and Explainability of LLMs Via Evolutionary Methods
Evolutionary trees from LLM weights recover ground-truth training topologies and identify key datasets and layers through phenotypic analysis.
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What Linear Probes Miss: Multi-View Probing for Weight-Space Learning
MVProbe is a multi-perspective probing framework for weight-space learning that combines first-order and Gram-based views and outperforms ProbeX on the Model Jungle benchmark.