Geometric Evolution Maps: Extracting Stable Concept Probes from Transformer Residual Streams
arXiv:2605.25848v1 Announce Type: new Abstract: Concept probes extracted from transformer residual streams are only as reliable as the layer from which they are extracted. The common practice of probing at a fixed late layer or at the peak of a separation score function ignores a fundamental structural feature: concept representations undergo substantial directional rotation during their assembly phase, and do not settle into a stable direction until a characteristic handoff layer after the primary Concept Allocation Zone (CAZ). We introduce Geometric Evolution Maps (GEMs), which track the ful
The increasing sophistication of transformer models and the growing investment in AI interpretability research drives the continuous development of better methods to understand internal representations.
Improved methods for probing concept representations in large language models enhance our ability to debug, control, and ensure the safety and reliability of advanced AI systems, impacting their deployment and societal trust.
The introduction of Geometric Evolution Maps (GEMs) offers a more robust and stable way to extract conceptual insights from transformer models, potentially leading to more reliable AI interpretability and alignment techniques.
- · AI Safety Researchers
- · ML Explainability Platforms
- · Developers of foundational AI models
- · AI systems with opaque or unstable internal representations
- · Researchers relying on less-robust concept probing methods
Researchers gain a more accurate and stable method to understand how transformers represent concepts internally.
This improved understanding facilitates better alignment, debugging, and targeted interventions in complex AI models.
More interpretable and controllable AI systems could accelerate adoption in sensitive applications and potentially influence future regulatory frameworks for AI safety.
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Read at arXiv cs.LG