A new arXiv paper proposes a retrieve-localize-generate pipeline for retrieval-augmented generation aimed at answering questions over long-term conversation history. The authors argue that current RAG methods fall short in this setting and frame their approach as addressing those gaps. The work appears as a replacement submission on arXiv's computation and language listing.
A new arXiv preprint examines knowledge graph question answering in settings where no single organization holds the entire graph. The authors describe a scenario in which parties share entity identifiers but own disjoint sets of relations, so federated methods are needed to reason across them. The paper presents design lessons from a working system and an interactive prototype.
A new arXiv paper introduces HyperProve, a method for multi-hop question answering that builds hypergraphs instead of treating retrieved passages as separate matches to the original question. The approach expands the evidence graph using the candidate answer as a guide, so that intermediate entities, relations, and constraints linking facts can be captured. The authors argue this better reflects how the information needed for complex questions is connected.
A new arXiv preprint introduces an approach for multi-hop knowledge graph question answering that emphasizes how a model travels through a graph rather than only the answer it produces. The authors argue that prior systems typically optimize for final-answer accuracy, leaving the relational evidence path unexplained. The work, titled "Theseus in the Graph," aims to make those navigation steps traceable.
A new arXiv paper presents CiteGuard-RAG, a retrieval-augmented generation system built around validating answers rather than only retrieving supporting passages. The authors argue that fetching evidence does not by itself guarantee that responses are properly grounded, carry valid citations, or are refused when appropriate. The work proposes a validation-focused design intended to enforce those checks.
A new arXiv paper addresses the scarcity of high-quality rationales in medical question-answering datasets, where answer labels are plentiful but explanations are expensive to validate. The authors reframe the data acquisition problem as deciding which already-labeled questions warrant rationales, using a perturbation-sensitive selection criterion. The approach aims to improve QA robustness by targeting rationale annotation where it has the greatest effect.