An LLM network-monitoring agent experienced nearly doubled telemetry delays under replayed DoS traffic, and edited memory files led it to choose longer, heavier packet captures, in a two-case test of the MAESTRO threat framework.
Survey on Memory-Augmented Neural Networks: Cognitive Insights to AI Applications
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abstract
This paper explores Memory-Augmented Neural Networks (MANNs), delving into how they blend human-like memory processes into AI. It covers different memory types, like sensory, short-term, and long-term memory, linking psychological theories with AI applications. The study investigates advanced architectures such as Hopfield Networks, Neural Turing Machines, Correlation Matrix Memories, Memformer, and Neural Attention Memory, explaining how they work and where they excel. It dives into real-world uses of MANNs across Natural Language Processing, Computer Vision, Multimodal Learning, and Retrieval Models, showing how memory boosters enhance accuracy, efficiency, and reliability in AI tasks. Overall, this survey provides a comprehensive view of MANNs, offering insights for future research in memory-based AI systems.
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Securing Agentic AI: Threat Modeling and Risk Analysis for Network Monitoring Agentic AI System
An LLM network-monitoring agent experienced nearly doubled telemetry delays under replayed DoS traffic, and edited memory files led it to choose longer, heavier packet captures, in a two-case test of the MAESTRO threat framework.