ILP-guided CEGIS evolves deployed expert safety rules for LLM agents from annotated traces, raising F1 to 0.98 (code) and 0.93 (embodied) in 4–5 iterations.
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A governed upgrade framework with interface, policy, behavioral, and recovery checks keeps unsafe activations at zero across multi-round AI capability upgrades on a PyBullet/ROS 2 manipulation testbed while retaining task success near naive upgrades.
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AutoSpec: Safety Rule Evolution for LLM Agents via Inductive Logic Programming
ILP-guided CEGIS evolves deployed expert safety rules for LLM agents from annotated traces, raising F1 to 0.98 (code) and 0.93 (embodied) in 4–5 iterations.
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Governed Capability Evolution: Lifecycle-Time Compatibility Checking and Rollback for AI-Component-Based Systems, with Embodied Agents as Case Study
A governed upgrade framework with interface, policy, behavioral, and recovery checks keeps unsafe activations at zero across multi-round AI capability upgrades on a PyBullet/ROS 2 manipulation testbed while retaining task success near naive upgrades.