A logic-based verifier using temporal annotated logic raises the share of LLM-proposed vulnerability exploration paths that satisfy domain knowledge from 78% to 98%.
Age-Aware Status Update Control for Energy Harvesting IoT Sensors via Reinforcement Learning
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abstract
We consider an IoT sensing network with multiple users, multiple energy harvesting sensors, and a wireless edge node acting as a gateway between the users and sensors. The users request for updates about the value of physical processes, each of which is measured by one sensor. The edge node has a cache storage that stores the most recently received measurements from each sensor. Upon receiving a request, the edge node can either command the corresponding sensor to send a status update, or use the data in the cache. We aim to find the best action of the edge node to minimize the average long-term cost which trade-offs between the age of information and energy consumption. We propose a practical reinforcement learning approach that finds an optimal policy without knowing the exact battery levels of the sensors.
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EntailLLM: Verifying LLM-Generated Vulnerability Discovery Paths with Domain Knowledge via Logic Programming
A logic-based verifier using temporal annotated logic raises the share of LLM-proposed vulnerability exploration paths that satisfy domain knowledge from 78% to 98%.