Epistemic Sybil Resistance: Multiplying AI Agents Without Multiplying Evidence

A new arXiv paper (2609.01873v1) formalizes an 'epistemic Sybil problem' in multi-agent AI systems, showing that adding more AI agents does not add independent evidence when reports share common ancestry. In controlled tests with over 20,000 LLM-agent calls, increasing report multiplicity from 1 to 32 while holding one evidence root collapsed naive posterior coverage from 0.940 to 0.263, while increasing evidence-root multiplicity from 1 to 16 made aggregators statistically indistinguishable at k=16. The authors argue collective inference should track evidential ancestry and dependence, not agent or report multiplicity.

arXiv:2609.01873v1 Announce Type: new Abstract: Multi-agent AI systems improve inference by spawning agents and synthesizing reports. But another agent is not another observation: apparently independent reports may descend from the same evidence, and genuinely independent evidence can produce nearly identical reports. We formalize this as an epistemic Sybil problem. A report Z is an epistemic Sybil extension relative to reports R when I Theta; Z | R = 0. No report-only aggregator can generally distinguish replication from independent corroboration: identical reports can warrant different posteriors under unobserved ancestry. A Gaussian shared-root model shows common ancestry does not imply complete redundancy. Repeated extraction adds information toward a source-level ceiling, and correlated extraction errors, which a shared base model can induce among independent agents, lower that ceiling further. We test these predictions with more than 20,000 controlled LLM-agent report and extraction calls on synthetic evidentiary documents. Holding one evidence root fixed while report multiplicity rises from 1 to 32 collapses naive posterior coverage from 0.940 to 0.263. Holding report count fixed while evidence-root multiplicity rises from 1 to 16 closes the gap, and the aggregators are statistically indistinguishable at k = 16. The agent's replicate extraction errors are correlated gamma cal = 0.719, estimated out of sample , and a correlated-extraction aggregator restores calibration accordingly. A controlled manipulation isolates representation similarity from evidential ancestry. It changes a report-space deduplication mechanism's mean inferred cluster count by 1.425 95% CI 1.363, 1.485 , whereas a fourfold change in true ancestry changes it by only 0.040 -0.045, 0.120 . Collective inference should therefore track evidential ancestry and dependence, not agent or report multiplicity or similarity.