# LLM Semantic Signaling Game and Mechanism Design: Systematic Blindness, Awareness Shaping, and Mindset Dynamics

arXiv: https://arxiv.org/abs/2606.29113 · HTML: https://mlsanity.com/paper/2606.29113/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 27 Jun 2026.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Playing repeated games with Large Language Models](https://mlsanity.com/paper/2305.16867/) (2023)
- [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024)
- [Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats](https://mlsanity.com/paper/2507.10621/) (2025)
- [On the Price of Transparency: A Comparison between Overt Persuasion and Covert Signaling](https://mlsanity.com/paper/2304.00096/) (2023)
- [Internet of Agentic AI: Incentive-Compatible Distributed Teaming and Workflow](https://mlsanity.com/paper/2602.03145/) (2026)
- [ADVERT: An Adaptive and Data-Driven Attention Enhancement Mechanism for Phishing Prevention](https://mlsanity.com/paper/2106.06907/) (2021)
- [Reasoning and Behavioral Equilibria in LLM-Nash Games: From Mindsets to Actions](https://mlsanity.com/paper/2507.08208/) (2025)
- [Modeling and Analysis of Leaky Deception using Signaling Games with Evidence](https://mlsanity.com/paper/1804.06831/) (2018)
- [Game-Theoretic Neyman-Pearson Detection to Combat Strategic Evasion](https://mlsanity.com/paper/2206.05276/) (2022)
- [LLM-Stackelberg Games: Conjectural Reasoning Equilibria and Their Applications to Spearphishing](https://mlsanity.com/paper/2507.09407/) (2025)
- [Phishing for Phools in the Internet of Things: Modeling One-to-Many Deception using Poisson Signaling Games](https://mlsanity.com/paper/1703.05234/) (2017)

## Abstract (arXiv)

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 blindness. The framework connects prompt-based control, statistical detection, and game-theoretic equilibrium analysis. Gaussian approximations of aggregate message scores enable likelihood-ratio decision rules, while Perfect Bayesian Nash equilibria characterize strategic behavior. The paper further develops mechanism-design approaches that reshape receiver awareness, penalize deceptive semantic controls, and modify receiver populations to induce benign pooling equilibria. Numerical experiments validate the Gaussian approximation, quantify awareness-ordering effects, analyze mindset dynamics under adaptive adversaries, and demonstrate how awareness shaping and guardrail costs reduce successful phishing attacks. The proposed framework provides a principled foundation for analyzing strategic language-mediated interactions in agentic AI systems and offers new tools for the design of robust and secure human-AI communication.