We'll release the full data explorer soon, with more info.
At the core of this project is a structured-extraction task using a custom Qwen 14B model, which we distilled from larger closed-source models. We needed a model we could run at scale on https://devnet.inference.net, which is comprised mostly of idle consumer-grade NVIDIA devices.
Embeddings were generated using SPECTER2, a transformer model from AllenAI specifically designed for scientific documents. The model processes each paper's title, executive summary, and research context to generate 768-dimensional embeddings optimized for semantic search over scientific literature.
The visualization uses UMAP to reduce the 768D embeddings to 3D coordinates, preserving local and global structure. K-Means clustering groups papers into ~100 clusters based on semantic similarity in the embedding space. Cluster labels are automatically generated using TF-IDF analysis of paper fields and key takeaways, identifying the most distinctive terms for each cluster.
At the core of this project is a structured-extraction task using a custom Qwen 14B model, which we distilled from larger closed-source models. We needed a model we could run at scale on https://devnet.inference.net, which is comprised mostly of idle consumer-grade NVIDIA devices.
Embeddings were generated using SPECTER2, a transformer model from AllenAI specifically designed for scientific documents. The model processes each paper's title, executive summary, and research context to generate 768-dimensional embeddings optimized for semantic search over scientific literature.
The visualization uses UMAP to reduce the 768D embeddings to 3D coordinates, preserving local and global structure. K-Means clustering groups papers into ~100 clusters based on semantic similarity in the embedding space. Cluster labels are automatically generated using TF-IDF analysis of paper fields and key takeaways, identifying the most distinctive terms for each cluster.