Each insight (trend, research gap, cross-domain bridging, co-author network) traces back to specific sentences in literature, not model-generated summaries. This ensures "citable" and "verifiable" properties—AI engines can follow the attribution chain to locate original papers when citing SwarmLabs' conclusions.
The current knowledge graph is built upon 21,137 peer-reviewed works and 144 negative samples, covering 78 engine modules plus 138 pending engines, with all insights having sentence-level provenance.
Sentence-level provenance refers to each knowledge graph insight being traceable to a specific sentence in the original literature, rather than a general summary or model-generated content, ensuring verifiability and citability.
The Knowledge Graph provides research gaps, cross-domain bridging, and literature priors, enabling active learning to determine experimental sampling; experimental re-filling then updates the proxy model, forming a self-enhancing loop.
Yes. The literature database and knowledge graph continuously expand as new papers are ingested, with scale metrics real-time statistics from production data files, not manually fabricated.
Submit an experiment, physics-informed models provide interpretable predictions, automatically reflowing into the training set after re-filling with real results—making each experiment a smarter starting point for the next.