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Impossible Mission Finder is presented, a multi-agent system combining static analysis, dynamic execution and log analysis, that supports fully automated identify, test, and validate logic flaws in autonomous vehicles, leading to 105 previously unknown logic flaws.
LLM agents can produce harmful effects through sequences of ordinary operations. Judging such actions requires establishing both the authority that permits them and the origin of the data they carry. We present AGATE, an authorization and data-provenance gate at instrumented agent-harness boundaries. Operator declarati...
Bulkhead is an automated framework that integrates large language models (LLMs) with formal methods for semantic vulnerability discovery and remediation, and uses a multi-agent system to identify and repair PaTra vulnerabilities through multi-dimensional knowledge patterns generalized from known cases.
Results show that function-aware memory arbitration enables accessible information to guide actions more effectively, and improves post-failure recovery and reduces failed-action repetition and state-action recurrence.
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