LOCARD: An Agentic Framework for Blockchain Forensics
Blockchain forensics inherently involves dynamic and iterative investigations, while many existing approaches primarily model it through static inference pipelines. We propose a paradigm shift towards Agentic Blockchain Forensics (ABF), modeling forensic investigation as a sequential decision-making process. To instantiate this paradigm, we introduce LOCARD, the first agentic framework for blockchain forensics. LOCARD operationalizes this perspective through a Tri-Core Cognitive Architecture that decouples strategic planning, operational execution, and evaluative validation. Unlike generic LLM-based agents, it incorporates a Structured Belief State mechanism to enforce forensic rigor and ...
| Authors | Xiaohang Yu, William Knottenbelt |
|---|---|
| Primary category | cs.CR |
| Published | 5 April 2026 |
| Last updated at source | 6 May 2026 |
Models and datasets referencing this paper
- xhyumiracle/thor25Dataset
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