Hosted large language model services increasingly sell access on the promise of a specific model identity, yet the people using those services have no direct way to see which model configuration actually handled their request. A new paper on arXiv, titled "Auditor-Assisted Summary-Channel Verification for Hosted LLM Identity Substitution," takes up this problem.
The paper examines an auditor-assisted regime in which a trusted third party helps verify the model identity behind a served request. While the abstract is brief, it makes clear that the current lack of inspectability is a real limitation for both external users and auditors, and that the proposed regime is meant to address that gap.
If such verification becomes practical, it could strengthen accountability for hosted LLM providers and give customers a firmer basis for trusting that the model they paid for is the model they get. The work is preliminary, but it points to a concrete direction for solving an increasingly important verification problem in the AI industry.