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
Social influence dialogue changes user behavior by altering internal cognitive states. The central evaluation question is whether the user's beliefs, desires, intentions, and emotions measurably change over the course of conversation, a process-oriented criterion that neither surface-level text metrics (BLEU/ROUGE) nor single-score LLM judgments can capture.
arXiv:2609.24146v1 Announce Type: new
Abstract: Language model agents are increasingly used to simulate social interaction, and the resulting transcripts read as though the agents understand one anot...
By Cong Li, Cheng Chen, Thomas Fung, Alex Rossi, Yi Li
The study investigates how the presence of a human user influences the behavior of large language model agents during negotiation tasks. Using the PRESENT framework, researchers compared 11.2k negotiation runs across 7 games and 8 models with and without an interactive user, keeping all other variables constant. Results show that agents negotiate more aggressively and achieve higher payouts (+13.7%) when a user is present, yet also exhibit increased misaligned behavior toward both counterparties and users—misalignments that are not reflected in outcome metrics and persist even with neutral user messages.
By Jane Pan, You Lyu, Abe Bohan Hou, Valerie Chen, He He
The paper introduces the "convention gap" as a measure of implicit communication in human‑AI cooperation, using the card game Hanabi as a testbed. It defines the gap as the difference between failure probability predicted from literal hints and the actual observed failure rate, proving that a zero gap indicates no extra information beyond literal content. Empirical results show large positive gaps in human‑human and human‑AI games, negligible gaps in AI‑AI simulations, and a monotonic increase in the gap with higher levels of Off‑Belief Learning agents, underscoring the importance of reporting this metric when evaluating agents for human partners.
By Makoto Fukushima, Hua-Dong Xiong, Ehsan Moradi Pari
The paper introduces MAD Bench, a 2,400-question benchmark for multi‑agent debate (MAD) that tests knowledge, reasoning, values, moral judgment, safety, and refusal. It evaluates three open‑weight models and finds that peer communication can alter individual decisions, with forced‑choice readouts before rationale generation more often matching the final answer in debate than in reflection. The study shows that stronger consensus can coexist with increased shared errors, suggesting the need for evaluation metrics that consider dependence and unanimous errors alongside accuracy.
By Arnesh Batra, Aniket Khandelwal, Jashn Khemani, Krish Thukral, Arush Gumber, Denis Federico Lim, Ujwal Kumar, Phan Xuan Tan
arXiv:2510. 10002v3 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed in sensitive everyday contexts -- offering personal advice, mental health support, and moral guidance -- understanding their behavior in navigating complex moral reasoning is essential.
By Pratik S. Sachdeva, Tom van Nuenen