Hop, Skip, and Overthink: Diagnosing Why Reasoning Models Fumble during Multi-Hop Analysis
Anushka Yadav, Isha Nalawade, Srujana Pillarichety, Yashwanth Babu, Reshmi Ghosh, Samyadeep Basu, Wenlong Zhao, Ali Nasaeh, Sriram Balasubramanian, Soundararajan Srinivasan
2025-08-08
Summary
This paper talks about why reasoning models sometimes fail when they have to answer questions that need several steps of thinking, called multi-hop analysis, and introduces a way to understand and fix these mistakes.
What's the problem?
The problem is that when language models try to connect multiple pieces of information to answer complex questions, they often make errors by overthinking, missing important steps, or jumping to wrong conclusions.
What's the solution?
The solution was to create a framework that categorizes the different types of errors models make during multi-step reasoning and uses this insight to help improve the accuracy and trustworthiness of their answers.
Why it matters?
This matters because better understanding and fixing these reasoning mistakes can make AI models smarter and more reliable, especially for tasks that require careful thinking and linking of ideas across several steps.
Abstract
Research investigates reasoning failures in language models for multi-hop question answering, introducing a framework to categorize errors and improve model fidelity.