arXiv:2607.04525v1 Announce Type: cross Abstract: How concepts are represented in neural networks is a fundamental question in machine learning. The dominant view treats concept representations as stationary geometric objects. Yet concepts appear in context, and context transforms them. Drawing from neural population geometry, we formalize concept representations as point-cloud manifolds and contextual transformations as vector fields, and instantiate this framework in large language models. Across six model families of varying scales, we find that context moves each concept differently. The v
This research provides a foundational understanding of how context influences concept representation in large language models, a critical area of inquiry as AI systems become more sophisticated.
Understanding the dynamic nature of concept representation and transformation in LLMs is crucial for developing more robust, interpretable, and adaptable AI agents.
This perspective shifts the understanding of AI concept representation from static geometric objects to dynamic, context-dependent manifolds and vector fields, suggesting new pathways for AI development.
- · AI researchers
- · LLM developers
- · NLP applications
- · Developers reliant on static concept embeddings
Improved performance and interpretability of large language models through a deeper understanding of contextual concept transformation.
Development of new architectural paradigms for AI models that explicitly account for dynamic concept representation.
Accelerated progress towards more agentic AI systems that can reason more effectively in complex, context-rich environments.
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Read at arXiv cs.AI