MSHL learns higher-order group relations from incomplete spatiotemporal observations via adaptive multi-scale hypergraph Laplacians and a safe neural refinement stage that improves imputation when structure is present.
Learning on graph with laplacian regularization.Advances in neural information processing systems, 19, 2006
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A framework combining universal AST normalization, hybrid graph-LLM embeddings, and strict execution-grounded validation achieves 89-92% intra-language accuracy and 74-80% cross-language F1 while resolving 70% of vulnerabilities at 12% failure rate.
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Learning Higher-Order Structure from Incomplete Spatiotemporal Data: Multi-Scale Hypergraph Laplacians with Neural Refinement
MSHL learns higher-order group relations from incomplete spatiotemporal observations via adaptive multi-scale hypergraph Laplacians and a safe neural refinement stage that improves imputation when structure is present.
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Verify Before You Fix: Agentic Execution Grounding for Trustworthy Cross-Language Code Analysis
A framework combining universal AST normalization, hybrid graph-LLM embeddings, and strict execution-grounded validation achieves 89-92% intra-language accuracy and 74-80% cross-language F1 while resolving 70% of vulnerabilities at 12% failure rate.