arXiv:2510.13796v3 Announce Type: replace Abstract: Symbol grounding (Harnad, 1990) describes how symbols such as words acquire their meanings by connecting to real-world sensorimotor experiences. Recent work has shown preliminary evidence that grounding may emerge in (vision-)language models trained at scale without using explicit grounding objectives. Yet, the specific loci of this emergence and the mechanisms that drive it remain largely unexplored. To address this problem, we introduce a controlled evaluation framework that systematically traces how symbol grounding arises within the inter
The paper investigates the mechanistic emergence of symbol grounding in large language models, a critical step toward more robust and human-like AI understanding. This research builds on preliminary evidence of grounding emergence without explicit objectives, pushing the boundaries of current AI capabilities.
Understanding how language models acquire meaning by connecting to real-world experiences is fundamental to developing truly intelligent AI that can operate in complex environments. This research provides a framework for analyzing and potentially engineering symbol grounding, accelerating the development of advanced AI agents.
This research provides a systematic framework to understand and trace the emergence of symbol grounding in AI models, moving beyond anecdotal evidence to mechanistic understanding. This could lead to more efficient and reliable methods for creating AI that truly 'understands' its environment, rather than merely processing symbols.
- · AI researchers
- · NLP developers
- · Robotics companies
- · Computer vision companies
- · Companies relying on purely symbolic AI systems
- · AI approaches ignoring grounding
- · Manual feature engineering in some AI tasks
Improved understanding of how language models link text to real-world concepts, leading to more robust and less 'hallucinatory' AI.
Accelerated development of general-purpose AI agents capable of more nuanced interaction with and understanding of the physical world.
Potential for new AI architectures that inherently integrate grounding from foundational training, reducing the need for post-hoc alignment or calibration.
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Read at arXiv cs.CL