Researchers have found that errors in multi-agent language models can spread and become more difficult to detect as they pass through the pipeline. They call this phenomenon the 'hallucination snowball effect'. The team used a financial analysis pipeline with 346 injected hallucinations and found that detection rates dropped from 72% at the first stage to around 50% by the fourth stage, with some errors remaining undetected in the final output. The study suggests that verifying models at intermediate stages is more effective than checking only at the end of the pipeline. --- Why it matters: This research matters because it highlights the limitations and vulnerabilities of multi-agent language models, which are increasingly used in applications such as finance and healthcare. Understanding how errors propagate through these systems can help developers design better verification strategies to prevent catastrophic failures. Source: https://arxiv.org/abs/2608.14588