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University of Science and Technology of China, University of Science and Technology
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LLM-based penetration testing agents can achieve up to 90% success in exploiting vulnerabilities, but their reconnaissance capabilities plateau at 50%, revealing a critical gap in automated security assessments.
SemGeoNav achieves superior obstacle avoidance and navigation efficiency by merging semantic reasoning with geometric planning, outperforming leading methods in real-world tests.