Sources Count, Copies Win: A Gap in LLM Evidence Aggregation

Large language model (LLM) agents search documents and exchange messages to gather evidence, yet duplicated study reports and recirculated messages can turn one observation into apparent consensus. Human experiments show that repetition can increase perceived truth even when people demonstrate knowledge of the correct facts in a separate test. We investigate whether LLM decisions remain vulnerable when shared provenance is explicit and source-counting ability is assessed separately. Our Explicit Evidence Groups task varies redundant records while holding independent support and the correct answer fixed. Across seven different LLMs, increasing a minority source from one to sixteen records reduces decision accuracy by 17.2–40.6 percentage points, even without repeated answer labels. Linear probes recover source-count and repetition information before a decision, and separate numerical reports often preserve the correct source ordering despite counting errors. Head selection and causal ablations reveal partially distinct attention-head contributions to source reporting and direct decisions. Reporting source counts before choosing improves decisions under repetition when the task remains within the models' counting capacity. Our findings show that explicit provenance alone cannot ensure reliable evidence aggregation: information about independent sources can be available without reliably constraining decisions. For scientific synthesis and multi-agent deliberation, reliability requires preserving the distinction between evidence that gains independent support and evidence that merely gains circulation.