# Epistemic Sybil Resistance: Multiplying AI Agents Without Multiplying Evidence

arXiv: https://arxiv.org/abs/2609.01873 · HTML: https://mlsanity.com/paper/2609.01873/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 1 Sep 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 8 of 8)

- [Securing LLM-Agent Long-Term Memory Against Poisoning: Non-Malleable, Origin-Bound Authority with Machine-Checked Guarantees](https://mlsanity.com/paper/2606.24322/) (2026)
- [When Truth Is Distributed: Misinformation Derails Collective Fact Recovery in LLM-Based Multi-Agent Systems](https://mlsanity.com/paper/2608.03421/) (2026)
- [Mesh Inference: A Formal Model of Collective Inference Without a Center](https://mlsanity.com/paper/2606.19537/) (2026)
- [Split the Labor: Separating Evidence Interpretation from Decision Aggregation](https://mlsanity.com/paper/2608.14509/) (2026)
- [Breaking the Illusion of Artificial Consensus: Clone-Robust Weighting for Arbitrary Metric Spaces](https://mlsanity.com/paper/2602.24024/) (2026)
- [Overlapping Covariance Intersection: Fusion with Partial Structural Knowledge of Correlation from Multiple Sources](https://mlsanity.com/paper/2603.16768/) (2026)
- [Quotient Semivalues for False-Name-Resistant Data Attribution](https://mlsanity.com/paper/2605.07663/) (2026)
- [A Failure-Mode Benchmark for Polymorphic Sybil Poisoning in RAG](https://mlsanity.com/paper/2607.03739/) (2026)

## Built on by (most cited first, 3 of 3)

- [Engineering Reliable Commit Gates for Agentic AI: Cost-Aware Verification Portfolios under Common-Mode Data Failures](https://mlsanity.com/paper/2609.10969/) (2026)
- [Authorization for Self-Modifying AI Agent Populations: Conserving Authority across Replacement, Forking, and Rollback](https://mlsanity.com/paper/2610.00347/) (2026)
- [Copies or Sources? Measuring How LLM Aggregators Count Restated Evidence in Multi-Agent Systems](https://mlsanity.com/paper/2610.06192/) (2026)

## Abstract (arXiv)

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.