There has been a surge in the use of large language models (LLM) conversational agents to generate responses based on long-term history from multiple sessions.However, existing long-term open-domain dialogue datasets lack complex, real-world personalization and fail to capture implicit reasoning-where relevant information is embedded in subtle, syntactic, or semantically distant connections rather than explicit statements.In such cases, traditional retrieval methods fail to capture relevant context, and long-context modeling also becomes inefficient due to numerous complicated personarelated details.To address this gap, we introduce IMPLEXCONV, a large-scale long-term dataset with 2,500 examples, each containing approximately 100 conversation sessions, designed to study implicit reasoning in personalized dialogues.Additionally, we propose TAC-ITREE, a novel hierarchical tree framework that structures conversation history into multiple levels of summarization.Instead of brute-force searching all data, TACITREE enables an efficient, level-based retrieval process where models refine their search by progressively selecting relevant details.Our experiments demonstrate that TACITREE significantly improves the ability of LLMs to reason over long-term conversations with implicit contextual dependencies.I love playing sports like basketball and swimming.I'm going on a sports stadium tour!Aug 15, 2024 I broke my leg in a car accident...