Ethical constraint evidence on open-weight AI models decays with a half-life of 1.31 derivation steps on Hugging Face, creating a governance horizon at seven generations where 80% of models lack traceable information.
I., Creel, K
4 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
SemFin combines model configuration files with repository tags to impute missing metadata across 317k PTLMs, outperforming propagation baselines by up to 31.4% and expanding reuse and license lineage chains on 167k models.
Most open-source LLMs are consolidative rather than disruptive, with disruptive models more likely to be large-scale and produced via finetuning, as identified by a new Model Disruption Index on a reconstructed lineage network.
Position paper proposing Model Science as a discipline to systematically analyze AI model behavior beyond benchmarks, drawing analogies from cognitive science, neuroscience, medicine, and agriculture.
citing papers explorer
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A governance horizon for ethical-use constraints in open-weight AI models
Ethical constraint evidence on open-weight AI models decays with a half-life of 1.31 derivation steps on Hugging Face, creating a governance horizon at seven generations where 80% of models lack traceable information.
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Towards Imputation of Pre-Trained Language Model Metadata using Semantic Fingerprinting
SemFin combines model configuration files with repository tags to impute missing metadata across 317k PTLMs, outperforming propagation baselines by up to 31.4% and expanding reuse and license lineage chains on 167k models.
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Identifying Disruptive Models in the Open-Source LLM Community
Most open-source LLMs are consolidative rather than disruptive, with disruptive models more likely to be large-scale and produced via finetuning, as identified by a new Model Disruption Index on a reconstructed lineage network.
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The Case for Model Science: Verify, Explore, Steer, Refine
Position paper proposing Model Science as a discipline to systematically analyze AI model behavior beyond benchmarks, drawing analogies from cognitive science, neuroscience, medicine, and agriculture.