Open Domain Multi-hop Question Answering faces a dual compositionality challenge: reasoning over complex query structures and integrating evidence scattered across contexts. Despite recent advancements in Graph-based Retrieval-Augmented Generation (GraphRAG), persistent limitations in complex reasoning and retrieval inaccuracies continue to constrain the efficacy of multi-hop QA systems. We introduce HiGraAgent, a framework that unifies graph-based retrieval with adaptive reasoning. It constructs a Hierarchical Knowledge Graph (HiGra) with entity alignment, reducing redundancy by 34.5% while preserving expressiveness; employs HiGraRetriever, a hybrid graph-semantic retriever that consistently outperforms the strongest graph-based method across benchmarks; and integrates a dual-agent adaptive reasoning protocol where a Seeker and a Librarian dynamically coordinate retrieval and reasoning. Together, these innovations enable HiGraAgent to achieve 85.3% average accuracy on HotpotQA, 2WikiMultihopQA, and MuSiQue, surpassing the strongest prior system by 11.7%. Our results highlight the importance of reframing multi-hop QA as a problem of adaptive reasoning, offering a more robust and flexible paradigm for complex information seeking.

Large Language Models (LLMs) have shown strong capabilities in Question Answering (QA), but their effectiveness in high-stakes, closed-domain settings is often constrained by hallucinations and limited handling of vague or underspecified queries. These challenges are especially pronounced in Vietnamese, a low-resource language with complex syntax and strong contextual dependence, where user questions are often short, informal, and ambiguous. We introduce the Unified Retrieval Agent-Based System (URASys), a QA framework that combines agent-based reasoning with dual retrieval under the Just Enough principle to address standard, ambiguous, and unanswerable questions in a unified manner. URASys performs lightweight query decomposition and integrates document retrieval with a question–answer layer via a two-phase indexing pipeline, engaging in interactive clarification when intent is uncertain and explicitly signaling unanswerable cases to avoid hallucination. We evaluate URASys on Vietnamese and English QA benchmarks spanning single-hop, multi-hop, and real-world academic advising tasks, and release new dual-language ambiguous subsets for benchmarking interactive clarification. Results show that URASys outperforms strong retrieval-based baselines in factual accuracy, improves unanswerable handling, and achieves statistically significant gains in human evaluations for clarity and trustworthiness.