AutoACSL integrates CPG-based static analysis into LLM prompts to synthesize ACSL specs for C programs, reporting 98% generation success and 96% full proof ratio with Gemini-3 on 604 programs, with 24.7-51.7% gains over code-only baselines.
PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation
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The paper delivers a systematization of knowledge on AI agent-blockchain interactions via a bidirectional trust framework, an Agent-Blockchain Interaction Model, a five-dimensional evaluation lens, and nine identified open problems.
Chaintrix achieves 71.7% recall on 120 high-severity vulnerabilities in the EVMbench benchmark and outperforms the strongest frontier-model baseline by 26 percentage points through LLM pipelines grounded in a Cross-Contract Interaction Model and filtered by structural checks.
citing papers explorer
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AutoACSL: Synthesizing ACSL Specifications by Integrating LLMs with CPG-Based Static Analysis
AutoACSL integrates CPG-based static analysis into LLM prompts to synthesize ACSL specs for C programs, reporting 98% generation success and 96% full proof ratio with Gemini-3 on 604 programs, with 24.7-51.7% gains over code-only baselines.
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Toward Web 4.0: Bidirectional Trust between AI Agents and Blockchain
The paper delivers a systematization of knowledge on AI agent-blockchain interactions via a bidirectional trust framework, an Agent-Blockchain Interaction Model, a five-dimensional evaluation lens, and nine identified open problems.
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CHAINTRIX: A multi-pipeline LLM-augmented framework for automated smart-contract security auditing
Chaintrix achieves 71.7% recall on 120 high-severity vulnerabilities in the EVMbench benchmark and outperforms the strongest frontier-model baseline by 26 percentage points through LLM pipelines grounded in a Cross-Contract Interaction Model and filtered by structural checks.