READER: Dynamic LLM Provenance from Query-Varying Interactions
Quick summary
arXiv:2606.10794v3 Announce Type: replace Abstract: Existing black-box LLM provenance methods achieve comparability by querying every candidate model with the same diagnostic prompts. In deployment, auditors inherit a different evidence stream: heterogeneous prompt-response traces that arrive incrementally. We formalize dynamic black-box LLM provenance: after enrolling a fixed candidate ecosystem, attribute query-varying interactions at any available evidence budget. READER recovers comparability through a frozen proxy LLM. It projects proxy states aligned with response tokens onto length-norm
Key takeaways
- arXiv:2606.10794v3 Announce Type: replace Abstract: Existing black-box LLM provenance methods achieve comparability by querying every candidate model with the same diagnostic prompts.
- In deployment, auditors inherit a different evidence stream: heterogeneous prompt-response traces that arrive incrementally.
- We formalize dynamic black-box LLM provenance: after enrolling a fixed candidate ecosystem, attribute query-varying interactions at any available evidence budget.
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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