Due to their ability of follow natural language instructions, vision-language-action (VLA) models are increasingly preva- lent in the embodied AI arena, following the widespread suc- cess of their precursors—LLMs and VLMs. In this paper, we discuss 10 principal milestones in the ongoing develop- ment of VLA models—multimodality, reasoning, data, eval- uation, cross-robkot action generalization, efficiency, whole- body coordination, safety, agents, and coordination with hu- mans. Furthermore, we discuss the emerging trends of us- ing spatial understanding, modeling world dynamics, post training, and data synthesis—all aiming to reach these mile- stones. Through these discussions, we hope to bring attention to the research avenues that may accelerate the development of VLA models into wider acceptability.

Chia-Yu Hung, Navonil Majumder, Ambuj Mehrish, Soujanya Poria. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

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The intentional spread of misinformation can have serious consequences in our society. This motivates us to address the task of identifying sock puppet accounts (i.e. fabricated online personas) created by individuals or organizations with the intention of deceiving their target audience. By approaching the problem as authorship attribution, we develop a sock puppet detection framework that relies solely on the texts posted by sock puppets without using any meta-information. We employed a large pre-trained language model and used interpretability methods to extract linguistic cues to answer our research question. We curated a high-quality sock puppet dataset to enable a comprehensive study for the research communities where existing datasets may include false positives due to their approximate approaches in identifying sock puppets. This dataset enables us to study the research question of whether authorship attribution works on sock puppets’ written texts. Our experiment shows that our method remarkably outperformed human annotators by 31.8% points. Furthermore, we employed an interpretability method, Integrated Gradient, to extract linguistic cues from our model for explanations of why an account is a sock puppet or not.