LLM Semantic Signaling Game and Mechanism Design: Systematic Blindness, Awareness Shaping, and Mindset Dynamics
arXiv:2606.29113v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly mediate strategic interactions through natural language, making semantic control a critical element of communication and deception. This paper develops a semantic signaling game in which a sender selects a semantic control, an LLM generates a stochastic message, and a receiver evaluates the message using an awareness-dependent scoring mechanism. Receiver awareness is modeled as a type that determines which linguistic features are perceived and used for inference, providing a formal model of systematic b
The increasing sophistication and integration of LLMs into strategic communication makes understanding their 'semantic control' and potential for deception critically important as they mediate more interactions.
This research provides a formal framework to analyze how LLMs can shape perceptions and influence strategic interactions, moving beyond simple accuracy metrics to complex systemic behaviors in AI communication.
The explicit modeling of receiver 'awareness' as a type in LLM-mediated games changes how we understand the vulnerabilities and strategic levers in AI-driven communication, potentially leading to new defense mechanisms and attack vectors.
- · AI ethicists
- · Cybersecurity researchers
- · Behavioral economists
- · Government intelligence agencies
- · Unsophisticated AI users
- · Individuals susceptible to manipulation
- · Platforms without robust truthfulness measures
Understanding the 'semantic control' of LLMs allows for more robust design of AI systems that participate in strategic communication.
This understanding informs the development of countermeasures and detection methods for AI-driven information warfare and influence operations.
The concept of 'systematic blindness' in AI interaction could lead to new regulatory frameworks for AI communication and accountability standards for LLM developers.
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Read at arXiv cs.AI