This paper introduces a semantic signaling game to analyze how large language models (LLMs) mediate strategic interactions and deception. It models receiver awareness as a type that influences perception, formalizing "systematic blindness" and connecting prompt control with game-theoretic analysis. The research also proposes mechanism design approaches to reshape receiver awareness and penalize deceptive controls, aiming to reduce phishing attacks and enhance secure human-AI communication. AI
IMPACT Provides a theoretical framework for understanding and mitigating deception in LLM-mediated strategic interactions.
RANK_REASON The cluster contains an academic paper detailing a new theoretical framework and analysis of LLM interactions.
Read on arXiv cs.MA (Multiagent) →
- Large language models
- Mindset Dynamics
- Perfect Bayesian Nash equilibria
- semantic signaling game
- Systematic Blindness
- human-AI communication
- mechanism design
- phishing attacks
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