Contrastive-Difference CKA Reveals Concept-Specific Structural Alignment Across Language Model Architectures
arXiv:2606.16897v1 Announce Type: new Abstract: Do different LLM architectures encode high-level concepts in structurally compatible ways? We systematically characterize a geometric-functional universality dissociation: across multiple concept domains and architectural families, moderate geometric convergence coexists with near-perfect functional transfer. Using contrastive-difference CKA (CKA_Delta), a training-free diagnostic that computes kernel alignment on per-sample contrastive differences, we isolate concept-specific convergence from generic similarity -- achieving significant discrimin
The paper leverages recent advancements in CKA techniques to address a fundamental question in LLM interpretability, benefiting from the rapid development and deployment of diverse architectural families.
Understanding how different LLM architectures encode concepts and the potential for functional transfer is critical for designing more efficient, robust, and generalizable AI systems.
This research provides a new diagnostic tool (CKA_Delta) that isolates concept-specific convergence from generic similarity, offering a more nuanced understanding of architectural compatibility in LLMs.
- · AI researchers
- · LLM developers
- · AI hardware architects
- · Developers ignoring architectural compatibility
- · Inefficient LLM training paradigms
Improved understanding of how different large language models process and represent information.
Faster development and deployment of new LLM architectures with enhanced functional transfer capabilities.
Potential for an 'interoperability layer' for AI models, allowing different architectures to collaborate more effectively on complex tasks.
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Read at arXiv cs.CL