arXiv AI
Sep 28

Thinking Less to Simulate Better: Intuitive Prompting Improves LLM Agents Simulating Individual Social Media Reactions, Including Unfamiliar Content

The study evaluates how well language‑model agents can simulate individual social media reactions by comparing predictions under different prompt conditions. Eight Serbian participants’ reactions to 68 posts were recorded, and four language models were asked to predict these reactions using prompts that varied in profile content and instruction style. The results show that prompts emphasizing attitudinal content and intuitive, immediate responses yield the highest fidelity, outperforming demographic backstories and a crowd baseline, and suggesting that such agents could act as general‑purpose simulated users.

By Ljubisa Bojic, Tijana Stanic, Joerg Matthes, Agariadne Dwinggo Samala, Bojana Dinic, Jue Wang
arXiv Computation and Language
Aug 27

Reasoning or Rambling? Exploring the Effect of Thinking on Agent Persuasion

The paper investigates how explicit reasoning in Large Reasoning Models (LRMs) affects their ability to persuade and be persuaded. Experiments on objective and subjective tasks reveal a Persuasion Duality: reasoning boosts an agent’s persuasive power by about 21 percentage points while also making it less susceptible to incorrect persuasion by up to 10 percentage points. However, the study finds that persuasiveness often relies on superficial cues like response length and repetition rather than logical validity, and that persuasion can amplify or attenuate non‑linearly across multi‑hop agent chains. The authors also propose an attention‑guided prompt‑level adversarial argument detection method that improves agent robustness.

By Haodong Zhao, Jidong Li, Zhaomin Wu, Tianjie Ju, Zhuosheng Zhang, Bingsheng He, Gongshen Liu