Lifelog Question-Answering (QA) has become an important application of lifelogging, answering questions posed over personal lifelog data to help lifeloggers gain insight into their lives, from lifestyle analytics to past-event reflection and behavioural patterns. Building on our previous work on lifelog retrieval, we present MemoriEase4.0, our system for the Lifelog Search Challenge 2026 (LSC’26),which targets the QA task using reasoning large language models (LLMs). The system adopts a Retrieval-Augmented Generation (RAG) approach to answer questions. For complex questions that require aggregating information, such as counting or comparison across time, reasoning LLMs analyse the retrieved evidence step by step before producing an answer. At the live competition, MemoriEase 4.0 finished third overall among fifteen teams and recorded the strongest ad-hoc retrieval performance. Our analysis further identifies answer latency and aggregation accuracy as the principal challenges that remain for competitive lifelog QA. Beyond the challenge, the system aims to evolve into a complete assistant for everyday lifelog utilisation.