arXiv:2606. 27826v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly deployed as embodied planners in egocentric environments, where task success requires not only achieving instructed goals but also acting in socially appropriate ways.
By Shiyun Zhao, Xinwei Song, Tianyu Guo, Xiaomeng Gao, Mingyuan Liu, Xu Han, Yuanyuan Zhang, Zhenliang Zhang, Xue Feng, Bo Dai
arXiv:2606. 04806v1 Announce Type: cross Abstract: LLMs and agentic systems are increasingly deployed in social environments, making normative competence critical for safe and appropriate behavior.
By Sichao Li, Sai Ma, Daniel Kilov, Secil Yanik Guyot, Zhuang Li, Seth Lazar
The paper "Inferring the Unspoken: Aligning Embodied Agents with Implicit Preferences" addresses the challenge of natural-language instructions that omit details needed for embodied action. It introduces the Preference-based Planning (PbP) benchmark, comprising 5,000 evaluation groups and 290 preferences across three levels, to systematically evaluate agents’ ability to infer latent user preferences from a few demonstrations. The authors propose the two-stage Inferring the Unspoken (InTU) framework, which first verbalizes inferred preferences from multimodal demonstrations and then generates action plans conditioned on that explicit representation, showing that explicit verbalization improves alignment and robustness compared to direct end-to-end planning.
By Manjie Xu, Xinyi Yang, Wei Liang, Chi Zhang, Yixin Zhu
The paper introduces FISER, a framework that explicitly infers human goals and intentions before planning actions for AI agents to follow natural language instructions in collaborative embodied tasks. It employs Transformer-based models and is evaluated on the HandMeThat benchmark, outperforming end-to-end approaches and strong baselines such as Chain of Thought prompting. FISER achieves state‑of‑the‑art performance on this embodied social reasoning task.
By Yanming Wan, Yue Wu, Yiping Wang, Jiayuan Mao, Natasha Jaques
arXiv:2608. 07533v1 Announce Type: new Abstract: An embodied agent is an intelligent entity that interacts with its environment through a physical body.
By Gengyang Xu, Dongwei Xiao, Yiteng Peng, Shuai Wang
arXiv:2607. 07021v1 Announce Type: new Abstract: Humans continuously coordinate with others in dynamic interactions, often through implicit, hard-to-quantify social norms that act as shared tacit expectations among interacting agents.
By Yi Yang, Siyuan Liu, Xin Gao, Huamu Sun, Chao Liu, Qing Zhou, Bingbing Nie