AI RESEARCH
Failure Modes in Multi-Hop QA: The Weakest Link Effect and the Recognition Bottleneck
arXiv CS.AI
•
ArXi:2601.12499v2 Announce Type: replace Despite scaling to massive context windows, Large Language Models (LLMs) struggle with multi-hop reasoning due to inherent position bias, which causes them to overlook information at certain positions. Whether these failures stem from an inability to locate evidence (recognition failure) or integrate it (synthesis failure) is unclear. We