Rethinking AI Translation: The ABC Framework's Embodied Approach to Meaning
By Michael Carl, Takanori Mizowaki, Aishvarya Raj, Masaru Yamada, Devi Sri Bandaru, Yuxiang Wei, Xinyue Ren
Published on November 24, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on cs.CL updates on arXiv.org.
Summary
This article proposes a novel "ABC framework" for understanding the translating mind, moving beyond traditional representation-based models. Drawing on Extended Mind theory, radical enactivism, Predictive Processing, and (En)Active Inference, it posits translation as an enacted activity dynamically integrating affective, behavioral, and cognitive (ABC) processes. Instead of merely being extended, the translator's mind is argued to emerge through continuous brain-body-environment interactions, reframing translation as skillful, embodied participation in sociocultural practice where meaning is co-created in real time.
Why It Matters
For AI professionals, this research presents a profound challenge and opportunity for the future of Natural Language Processing (NLP) and AI systems that interact with human language. Current state-of-the-art AI translation models, while powerful, are largely rooted in representation-based paradigms-mapping static interlingual correspondences through vector embeddings and statistical patterns. This "ABC framework" offers a radical alternative, suggesting that true linguistic understanding and translation emerge from dynamic, embodied interaction rather than static data manipulation.
This paradigm shift implies that building more capable and nuanced AI translators may require moving beyond current transformer architectures to models that can dynamically integrate affective, behavioral, and cognitive processes within a continuous loop of "brain-body-environment" interactions. Such an approach could unlock AI systems capable of richer contextual understanding, handling ambiguity with human-like dexterity, and participating more genuinely in sociocultural practices. It pushes the boundaries of "embodied AI" and "predictive processing" in language, hinting at a future where AI's "understanding" isn't just about processing symbols, but about co-creating meaning through skillful, real-time engagement with the world. This fundamental re-evaluation of how intelligence and meaning emerge could lead to AI that is not only more effective but also more aligned with human cognition, fostering advancements in areas like conversational AI, human-robot interaction, and truly adaptive language agents.