8 papers
Optimal Stopping of Self-Refining Foundation Models
Kim Hammar, Tansu Alpcan, Emil C. Lupu
Foundation models can improve their outputs through a self-refinement process driven by external feedback. In this process, the model is embedded in an iterative loop where it gene…
Adaptive Network Security Policies via Belief Aggregation and Rollout
Kim Hammar, Yuchao Li, Tansu Alpcan +2
Evolving security vulnerabilities and shifting operational conditions require frequent updates to network security policies. These updates include adjustments to incident response…
Causal Online Learning of Safe Regions in Cloud Radio Access Networks
Kim Hammar, Tansu Alpcan, Emil Lupu
Cloud radio access networks (RANs) enable cost-effective management of mobile networks by dynamically scaling their capacity on demand. However, deploying adaptive controllers to i…
Hallucination-Resistant Security Planning with a Large Language Model
Kim Hammar, Tansu Alpcan, Emil Lupu
Large language models (LLMs) are promising tools for supporting security management tasks, such as incident response planning. However, their unreliability and tendency to hallucin…
Gen-Review: A Large-scale Dataset of AI-Generated (and Human-written) Peer Reviews
Luca Demetrio, Giovanni Apruzzese, Kathrin Grosse +4
How does the progressive embracement of Large Language Models (LLMs) affect scientific peer reviewing? This multifaceted question is fundamental to the effectiveness -- as well as…
Incident Response Planning Using a Lightweight Large Language Model with Reduced Hallucination
Kim Hammar, Tansu Alpcan, Emil C. Lupu
Timely and effective incident response is key to managing the growing frequency of cyberattacks. However, identifying the right response actions for complex systems is a major tech…