A Living Meta-Analysis Architecture for Active Inference: Assertion Extraction, Nanopublications, and Hypothesis Scoring

No prior automated system tracks hypothesis-level evidence across the full Active Inference and Free Energy Principle literature at scale. This work presents a living meta-analysis framework: literature is retrieved from arXiv, Semantic Scholar, and OpenAlex and deduplicated ( N = 819 in v2) via a canonical identifier hierarchy (DOI arXiv ID Semantic Scholar ID OpenAlex ID). Papers are classified into a three-tier taxonomy (A: core theory; B: tools and translation; C: application domains) across eight categories. An LLM-powered pipeline evaluates each abstract against eight core hypotheses, emitting structured nanopublications (directionality, confidence, reasoning) that populate an RDF-compatible knowledge graph scored by a citation-weighted evidence function. All extractions are machine-generated and not fully manually validated; hypothesis scores are preliminary and are most useful fo