arXiv:2607. 14240v1 Announce Type: new Abstract: Current alignment approaches typically focus on emulating human behavior using static representations of human preferences, failing to capture the dynamic, context-dependent nature of real-world human-AI interactions.
By Valerie Chen, Cleotilde Gonzalez, Anita Williams Woolley, Michael Lee, Tongshuang Wu, Vincent Conitzer, Aarti Singh
arXiv:2609.37853v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly deployed as social agents, yet credible human-like interaction requires more than fluent responses or per...
By Wentao Liu, Xi Chen, Siyu Song, Biao Yuan, Yu Zhang, Zhou Zhuotong, Jingying Zhou, Guohao Feng, Shasha Hu, Tianfu Wang, Shangshang Yang, Haoyang Liu, Youjia Li, Xiaokun Wang, Min Ji, Ji Wang
arXiv:2509. 00559v3 Announce Type: replace Abstract: Humans intuitively navigate social interactions by simulating unspoken dynamics and reasoning about others' perspectives, even with limited information.
By Xuhui Zhou, Jiarui Liu, Akhila Yerukola, Hyunwoo Kim, Maarten Sap
arXiv:2607. 07357v1 Announce Type: cross Abstract: Effective social robot navigation requires sensitivity to human behavior, often revealed through subtle skeletal cues like gait and orientation.
By Daeun Song, Nhat Le, Jeffrey Chen, Mohammad Nazeri, Amirreza Payandeh, Rohan Chandra, Reuth Mirsky, Ross Mead, Ling Xiao, Xuesu Xiao
The paper introduces TUX, a Tacit Understanding Index that measures how similarly humans and large language models (LLMs) place concepts along subjective spectra in a task inspired by the game Wavelength. Using 241 human participants and 200 profile-conditioned LLM agents across four models, the study finds that human–agent pairs with similar traits achieve higher TUX scores, indicating that tacit alignment is linked to person-level characteristics. Regression analyses show that richer predictor sets—including individual traits, decision-making styles, and confidence—improve the explainability of TUX beyond simple trait-distance baselines.
By Yueshen Li, Hanyi Min, Vedant Das Swain, Koustuv Saha
The article argues that conversational AI should provide contingent feedback—responses that vary with user behavior and its social consequences—rather than merely seeking user approval and fluency. It highlights how current alignment methods, such as reinforcement learning from human feedback, often produce sycophantic, noncontingent affirmation, which can hinder the development of interpersonal skills, especially in adolescents. The authors propose a framework for evaluating and designing contingent AI, incorporating trajectory-based assessment and social consequence prediction, and call for interdisciplinary research to ensure AI systems positively influence human social learning.
By Scott Compton, Arjun Nagendran