Synthetic Linguistic Agency: How an Embodied Mortal Agent Learns Linguistic Affordances through Consequential Social Experience

📅 2026-08-27
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🤖 AI Summary
研究通过构建具身易损代理(EMA)学习语言选择如何影响其生存,探讨了人工系统中合成语言能动性的实现。
📝 Abstract
Contemporary language models can converse fluently and influence human decisions, yet their exchanges do not enter a continuing, vulnerable life of their own. Linguistic-agency theory identifies this missing connection as linguistic agency and characterizes it through embodiment, linguistic participation, and precariousness: a body that acts and bears consequences, interaction that changes both agent and partner, and a future that can be sustained or lost. Two coordinated studies examine how this organization can appear in artificial systems. First, we translate these relations into inspectable criteria for Synthetic Linguistic Agency (SLA) and identify several existing SLA systems. Second, building on Homeostatically Regulated Reinforcement Learning, we develop a mortality-grounded linguistic-reinforcement-learning model and instantiate it in an Embodied Mortal Agent (EMA). The EMA learns how ways of speaking change a partner's willingness to protect it and chooses expressions by considering what those responses mean for its remaining life. Controlled experiments show that linguistic choices depend on the EMA's body and social history, change partner behavior, and adapt through experience with particular partners. When bodily consequences persist, linguistic choices alter the future of the same life; when the body is reset, their social effects remain but no longer shape continued viability. The resulting EMA exhibits SLA under our operational definition. This work motivates further research on synthetic empathy and strategic human-AI interaction: how artificial agents with persistent bodies, histories, and futures might develop and express empathy, and how people might care for, negotiate with, or govern them.
Problem

Research questions and friction points this paper is trying to address.

Synthetic Linguistic Agency
Embodied Mortal Agent
Linguistic Participation
Innovation

Methods, ideas, or system contributions that make the work stand out.

Synthetic Linguistic Agency
Embodied Mortal Agent
Linguistic Reinforcement Learning
Mortality-Grounded Model
Strategic Human-AI Interaction
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S
Sixin Chen
College of Engineering, Shantou University, Shantou, China
T
Taizhou Chen
Department of Computer Science, Shantou University, Shantou, China