Fault localization is essential for debugging. Recently, Large Language Model (LLM)-based techniques have been proposed to identify buggy methods from failing test cases. The state-of-the-art approach, \textit{SoapFL}, adopts a structured multi-phase procedure. Given a test failure, suspicious classes and methods are identified based on its analysis of the test failure. We conduct a funnel analysis of the multi-phase approach. While effective, our analysis highlights a limitation: the early selection of a single hypothesis can cause useful alternatives to be discarded. Motivated by how developers consider multiple competing hypotheses of the root cause of a bug as they analyze the evidence, we prototype \textit{HypoDeduce}, which applies abductive reasoning. \textit{HypoDeduce} generates and evaluates competing hypotheses, integrates evidence across classes and methods, and then identifies suspicious methods.

On Defects4J, our prototype successfully localizes the most suspicious method for 48.6% of bugs, improving over \textit{SoapFL} by 6%. These results suggest that structured hypothesis management enhances fault localization. We propose a research agenda for better supporting abductive analysis with more principled forms of reasoning supported by neurosymbolic approaches. In particular, we highlight the need for probabilistic reasoning, a combination of neural and symbolic methods for code navigation, as well as counterfactual analysis for suspicious code selection.