Presents the first fully open pipeline for clinical LLMs by unifying eight public QA datasets with three clinician-vetted synthetic extensions and applying it to five base models to achieve benchmark gains while maintaining auditability.
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8 Pith papers cite this work, alongside 16 external citations. Polarity classification is still indexing.
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Thematic analysis of r/LocalLLaMA discussions finds users define openness via reliability, local control, privacy, and adaptation under compute, licensing, and usability constraints.
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.
Open-source AI model development shows far less collaboration and openness than classic open-source software; users innovate by adapting models rather than improving them.
Longitudinal study of 56,800 AI papers finds sixfold increase in code+data sharing from 2014-2024 with inferred reproducibility rising from 28% to 64%.
A participatory workshop identified four core tensions in responsible AI openness and produced co-created visions, action pathways, and a research roadmap.
Reproducible builds, not share-alike code clauses, must ground user freedoms for AGI because current open-source frameworks cannot handle the legal and technical constraints of model artifacts.
ODYSSEY is a sheaf-theoretic framework for building verifiable foundation models as compositions of foundries via left and right Kan extensions.
citing papers explorer
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Fully Open Meditron: An Auditable Pipeline for Clinical LLMs
Presents the first fully open pipeline for clinical LLMs by unifying eight public QA datasets with three clinician-vetted synthetic extensions and applying it to five base models to achieve benchmark gains while maintaining auditability.
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Open AI in the Wild: Adoption and Adaptation of Open Models on r/LocalLLaMA
Thematic analysis of r/LocalLLaMA discussions finds users define openness via reliability, local control, privacy, and adaptation under compute, licensing, and usability constraints.
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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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From OSS to Open Source AI: an Exploratory Study of Collaborative Development Paradigm Divergence
Open-source AI model development shows far less collaboration and openness than classic open-source software; users innovate by adapting models rather than improving them.
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The Shift Toward Open and Reproducible AI Research
Longitudinal study of 56,800 AI papers finds sixfold increase in code+data sharing from 2014-2024 with inferred reproducibility rising from 28% to 64%.
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Reimagining Open Source and Openness in AI: Co-Creating Responsible Technological Futures
A participatory workshop identified four core tensions in responsible AI openness and produced co-created visions, action pathways, and a research roadmap.
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Reproducibility is the New Copyleft: Defining AGI-oriented Reproducible Builds
Reproducible builds, not share-alike code clauses, must ground user freedoms for AGI because current open-source frameworks cannot handle the legal and technical constraints of model artifacts.
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Odyssey: Constructing Verifiable Local Truth-Preserving Foundation Models
ODYSSEY is a sheaf-theoretic framework for building verifiable foundation models as compositions of foundries via left and right Kan extensions.