Visible but Not Yet Curatable: Characterizing the Curatability of Compact and Derived Open LLM Artifacts

Open Large Language Model (LLM) research increasingly produces compact and derived artifacts whose evidence is distributed across papers, model hubs, code repositories, and release records. Although these artifacts are often publicly visible, the evidence needed to identify, cite, and preserve them as coherent scholarly objects remains fragmented. We introduce curatability as a record-level property of distributed scholarly records and operationalize it through four evidence dimensions: artifact identity, scholarly linkage, upstream evidence, and release assets. Using a May 2026 snapshot of 191,375 public Hugging Face repositories and 2,214 scholarly paper records, we characterize open LLM curatability at collection scale. The results reveal a pronounced visibility-to-curatability funnel: 90.7% of paper records contain at least one useful curation signal, but only 18.1% combine usable upstream and release evidence, and only 6.1% provide sufficiently coordinated evidence for high-curatability records. Based on these findings, we derive a minimal seven-field curatable record and outline complementary responsibilities for model hubs, scholarly indexes, and digital libraries. The code and results are available at: https://github.com/AndyLu666/VNYC-Open_Source_LLM_Study.