# Adversarial Social Epistemology for Assemblies of Humans and Large Language Models

Published: 2026-07-11 · Updated: 2026-07-11 · Category: Research

Source page: https://www.sipply.dev/story/adversarial-social-epistemology-for-assemblies-of-humans-and-dc4e4df1 · 中文: https://www.sipply.dev/zh/story/adversarial-social-epistemology-for-assemblies-of-humans-and-dc4e4df1

## Summary

A new framework called adversarial social epistemology (ASE) has been introduced to better understand the dynamics of trust manipulation in environments where large language models (LLMs) interact with human users. This research critiques traditional concepts such as epistemic bubbles and misinformation diffusion, providing a fresh perspective on how trust can be breached in public communications. The ASE framework aims to analyze the interactions between humans and LLMs, highlighting the potential for these systems to influence perceptions and beliefs in significant ways.

## Key takeaways

- The ASE framework offers a novel approach to understanding trust dynamics in human-LLM interactions.
- It critiques existing theories on misinformation and trust, suggesting a need for updated models in the age of AI.
- The research emphasizes the importance of addressing trust manipulation in public communications involving AI.

## Why it matters

This research is significant as it addresses the growing concerns about trust and misinformation in the context of AI, particularly as LLMs become more integrated into everyday communication.

- **Product managers:** For product managers, understanding the ASE framework can help in designing AI systems that foster trust and mitigate misinformation risks, ensuring better user engagement and satisfaction.
- **Developers:** Developers should be aware of the implications of trust manipulation in AI systems, as it can inform the design and implementation of more responsible and ethical AI applications.
- **Students and job seekers:** Students and job-seekers in AI and related fields should recognize the importance of trust dynamics in AI systems, as this knowledge can enhance their understanding of ethical AI development and its societal impacts.

## Sources

- [Adversarial Social Epistemology for Assemblies of Humans and Large Language Models](https://arxiv.org/abs/2607.07760) (arXiv cs.AI, 2026-07-11)

## 中文

### 针对人类与大型语言模型的对抗性社会认识论

一种新的框架——对抗性社会认识论（ASE）被引入，以更好地理解大型语言模型（LLMs）与人类用户互动时信任操控的动态。该研究批判了传统概念，如认识泡沫和虚假信息传播，提供了关于如何在公共沟通中破坏信任的新视角。ASE框架旨在分析人类与LLMs之间的互动，强调这些系统在显著影响感知和信念方面的潜力。

**要点：**

- ASE框架为理解人类与LLM互动中的信任动态提供了新方法。
- 它批判了现有的虚假信息和信任理论，建议在AI时代需要更新模型。
- 该研究强调了在涉及AI的公共沟通中解决信任操控的重要性。

**为什么重要：** 这项研究的重要性在于它解决了关于AI背景下信任和虚假信息日益增长的担忧，尤其是随着LLMs越来越多地融入日常沟通。

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