arXiv:2606.07524v1 Announce Type: cross Abstract: The explosive growth of large language models (LLMs) has created a heterogeneous and poorly documented ecosystem, making systematic model comparison increasingly important for provenance auditing, security analysis, and model selection. Existing representation methods struggle to address this setting efficiently. Approaches analyzing internal parameters are powerful when architectures are compatible, but face scalability barriers under structural heterogeneity, while methods relying on external outputs may conflate models with similar behaviors
The explosive growth and increasing heterogeneity of LLMs necessitate new methods for systematic comparison and auditing, driven by concerns around provenance, security, and selection.
A robust method for representing and mapping LLMs directly addresses challenges in governance, security, and responsible deployment by making their internal workings more transparent and comparable.
The ability to accurately represent and map diverse LLMs, regardless of architectural compatibility, changes the landscape of model evaluation, auditing, and potentially, regulation.
- · AI auditors
- · Model developers
- · Regulatory bodies
- · Enterprises deploying LLMs
- · Opaque LLMs
- · Fragmented AI ecosystem
- · Security vulnerabilities
Improved transparency and comparability of large language models across different architectures and developers.
Increased trust and accelerated adoption of LLMs in critical applications due to better auditing and security analysis capabilities.
The emergence of industry standards for LLM representation and evaluation, leading to more standardized development and deployment practices.
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Read at arXiv cs.AI