The study examines how large language models (LLMs) predict users’ decisions in human‑agent dialogue by combining users’ statements with past decision records. Using 1,260 rounds of a repeated prisoner's dilemma with 42 users and five agents, the researchers found that decision records alone yielded strong predictive accuracy (AUC = .80), while adding dialogue did not improve overall performance but did increase agreement with users’ statements. However, predictions over‑emphasized statements, especially when users had not yet made a decision, and failed to capture variations in cooperation that depended on the specific agent or users’ trait alignment.
The paper investigates why large language models (LLMs) struggle in strategic decision-making under incomplete information. It identifies two key gaps: an observation‑belief gap where LLMs’ internal representations of game states are accurate but brittle, and a belief‑action gap where converting these internal beliefs into actions is weak, leading to suboptimal payoffs. Experiments with Llama 3.1, Qwen3, and gpt‑oss confirm that acting optimally on decoded beliefs would improve outcomes in most games, highlighting a bottleneck in belief‑to‑action conversion.
By Jan Sobotka, Mustafa O. Karabag, Ufuk Topcu
The study investigates whether large language model (LLM) agents can distinguish between user‑initiated corrections and manipulative pressure when deciding to concede. Across seven models from four vendors, researchers found that while citations increased willingness to concede, they also reduced discrimination between genuine corrections and manipulation. Additionally, concession on answers did not predict policy violations in a customer‑support scenario, indicating that mere concession rates are insufficient for evaluating agent reliability.
By Nardine Basta, Venus Haghighi, Dali Kaafar, Amin Beheshti
arXiv:2606. 29495v1 Announce Type: new Abstract: Social influence dialogue changes user behavior by altering internal cognitive states.
By Minghui Ma, Bin Guo, Han Wang, Mengqi Chen, Jingqi Liu, Yan Liu, Zhiwen Yu
arXiv:2608.18085v2 Announce Type: replace
Abstract: Prior work has shown that large language models can express diverse personality traits in open-ended text generation, but it is unclear whether the...
By Maryam Shoaeinaeini, Brent Harrison, A. B. Siddique
The study examines how Theory of Mind (ToM) reasoning and prosocial beliefs influence large language models (LLMs) in the ultimatum game. By initializing LLM agents with Greedy, Fair, or Selfless beliefs and applying chain‑of‑thought or varying levels of ToM reasoning, the authors ran 2,700 simulations across several models, including o3‑mini and DeepSeek‑R1 Distilled Qwen 32B. Results show that ToM‑enhanced LLMs align more closely with human decision patterns, exhibit greater consistency, and achieve better negotiation outcomes, with Llama 3.3 70B producing the most belief‑consistent reasoning.
whyItMatters":"The findings clarify the importance of incorporating Theory of Mind into LLMs to improve their alignment with human norms in cooperative decision‑making tasks."
By Neemesh Yadav, Yihuai Lan, Shan Dong, Mai Hieu Hien, Palakorn Achananuparp, Jing Jiang, Ee-Peng Lim