GeoFusion: Entanglement-Guided Fusion of Geometry-aware Representations and Spectral Priors for Code Summarization

Code summarization aims to generate concise and accurate natural language descriptions of source code. Recent methods already exploit multi-source heterogeneous program information, yet relations with distinct structures are still placed in one Euclidean space, and their geometric properties receive limited attention. We propose GeoFusion, an entanglement-guided method that fuses geometry-aware representations with spectral priors. GeoFusion encodes abstract syntax trees (ASTs) in a Poincaré ball to preserve hierarchical syntax. Data-flow and sequential relations remain in Euclidean space, which better fits non-hierarchical dependence. To align programs with natural language, GeoFusion organizes summary semantics, source-code functionality, and line-level comment functionality in a corpus-level spectral taxonomy graph, and derives a spectral taxonomy prior. Entanglement-guided adaptive fusion then uses coupling entropy to regulate contributions across sources and within each source. Experiments on Java and Python datasets show that GeoFusion achieves the state-of-the-art performance, improving existing methods by 4.61, 4.27 and 3.02 in terms of BLEU, ROUGE-L and METEOR on Java, and by 3.78, 3.52 and 2.52 on Python.