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About this paper
Marc Bara
This research introduces the "epistemic Sybil problem" to describe how generating numerous AI agent reports from limited underlying evidence leads to manufactured corroboration and overconfidence. It demonstrates that a naive aggregation of LLM agent outputs from a single source drastically reduces 95% credible interval coverage from 0.940 to 0.263, and that content-based deduplication is ineffective at discerning true evidential ancestry.
Key insights
- The "No-Minting Principle" establishes that any set of reports derived from fixed primitive evidence cannot contain more information about a latent state than the original evidence itself.
- Aggregators relying solely on report content or superficial similarity cannot reliably distinguish between exact replication and genuinely independent corroboration, leading to unavoidable errors.
- Information gain from multiple extractions from a single noisy source exhibits diminishing returns, converging to a finite ceiling determined by source noise and further limited by correlated extraction errors inherent to shared agent models.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.