Hound – Relation-First Knowledge Graphs for Complex-System Reasoning

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Hound is a code security auditing tool that draws inspiration from human cognitive processes to enhance reasoning about large, complex systems. By modeling the target application as relation-first knowledge graphs (e.g., monetary/value flows, authentication/authorization roles, call graphs, and invariants with compact annotations), the agent enables multi-granular attention: it can "zoom in" on specific graph slices for detailed analysis while maintaining a summarized view of the entire system through broad mappings. Complementing this is a persistent belief system that tracks hypotheses with explicit evidence and confidence levels, refining them over time as new evidence emerges—ensuring a disciplined lifecycle for findings that mirrors iterative human reasoning and belief updating. Evaluated on a subset of the ScaBench benchmark, Hound shows improvements in vulnerability detection (31.2% true positives versus 8.3% for a baseline LLM analyzer) and F1 score (14.2% versus 9.8%). While tailored for security audits, Hound's analyst-defined graphs and cognitive-inspired framework provide a solid basis for general complex-system reasoning. Released on September 15, 2025, the full paper is available on [Zenodo]( https://zenodo.org/records/17129271 ), with the implementation hosted on [GitHub]( https://github.com/scabench-org/hound ) for further exploration and reproduction.

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