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Proposes a message-passing-based two-timescale Bayesian deep learning framework for joint channel and memory hardware impairment tracking in massive MIMO systems.
This paper proposes a novel preference estimation method that integrates natural language information from LLMs into a structured Bayesian opponent modeling framework for multi-agent negotiation. The approach leverages LLMs to extract qualitative cues from utterances and convert them into probabilistic formats, demonstrating improved agreement rates and preference estimation accuracy on multi-party negotiation benchmarks.