Hello Again! LLM -powered Personalized Agent for Long-term Dialogue (2025)

Abstract: Open-domain dialogue systems have seen remarkable advancements with the development of large language models (LLMs). Nonetheless, most existing dialogue systems predominantly focus on brief single-session interactions, neglecting the real-world demands for long-term companionship and personalized interactions with chatbots. Crucial to addressing this real-world need are event summary and persona management, which enable reasoning for appropriate long-term dialogue responses. Recent progress in the human-like cognitive and reasoning capabilities of LLMs suggests that LLM-based agents could significantly enhance automated perception, decision-making, and problem-solving. In response to this potential, we introduce a model-agnostic framework, the Long-term Dialogue Agent (LD-Agent), which incorporates three independently tunable modules dedicated to event perception, persona extraction, and response generation. For the event memory module, long and short-term memory banks are employed to separately focus on historical and ongoing sessions, while a topic-based retrieval mechanism is introduced to enhance the accuracy of memory retrieval. Furthermore, the persona module conducts dynamic persona modeling for both users and agents. The integration of retrieved memories and extracted personas is subsequently fed into the generator to induce appropriate responses. The effectiveness, generality, and cross-domain capabilities of LD-Agent are empirically demonstrated across various illustrative benchmarks, models, and tasks. The code is released at https://github.com/leolee99/LD-Agent.

## introduction open-domain dialogue systems aim to establish long-term, personalized interactions with users * these authors contribute equally to this work. benefiting from the excellent human-like cognitive and reasoning abilities of large language models (llm), there is an increasing trend @xcite @xcite @xcite to employ llms as the cores of agent-based simulation systems to automate the process of perception, decisionmaking, and problem-solving. while recent studies have developed llm-powered agents in various fields, such as economics @xcite , politics @xcite , sociology @xcite , and recommendation @xcite , its application in open-domain dialogue remains unexplored. to effectively support long-term opendomain dialogue, an llm-powered dialogue agent framework should exhibit broad generality, crossdomain adaptability, and the ability to dynamically refine information across dimensions like events, user personalities, and agent personalities. in this paper, we propose ld-agent-a modelagnostic long-term dialogue agent framework consisting of three principal components: an event memory perception module, a persona extraction module, and response generation module (see the framework of ld-agent in figure 2 ). the event memory perception module is designed to enhance coherence across sessions by separately maintaining long-term and short-term memory banks. the long-term memory bank stores vector representations of high-level event summaries from previous sessions, refined through a tunable event summary module. the short-term memory bank maintains contextual information for ongoing conversations. the persona extraction module, designed to facilitate personalized interactions, incorporates a disentangled, tunable mechanism for accurate useragent modeling. extracted personas are continuously updated and stored in a long-term persona bank. these personas, along with relevant memories, are then integrated into the response generation module, guiding the generation of appropriate responses, as depicted in figure 1 . we conduct comprehensive experiments on two illustrative long-term multi-session daily dialogue datasets, msc @xcite and conversation chronicles (cc) @xcite , to evaluate the effectiveness, generality, and cross-domain capabil-ities of the proposed framework. in terms of effectiveness, ld-agent achieves state-of-the-art performance on both benchmarks, significantly outperforming existing methods @xcite @xcite . to assess generality, we examine the framework from both model and task perspectives. from the model perspective, ld-agent is evaluated across a range of both online and offline models, including llms @xcite and non-llms @xcite . from the task perspective, we extend our evaluation to multiparty dialogue tasks @xcite , where ld-agent also demonstrates substantial improvements, showcasing its adaptability across different models and tasks. regarding the method's crossdomain capabilities, we design two cross-domain settings: tuning the model on the msc dataset and testing it on the cc dataset, and vice versa. in both scenarios, ld-agent shows competitive performance, nearly matching the results of in-domain training. our contributions can be summarized as follows: • we develop ld-agent, a general long-term dialogue agent framework, considering both historical events, ensuring coherence across sessions and personas, ensuring character consistency. long-term dialogues. open-domain dialogue aims to develop a human-like chatbot that can emulate human conversation, facilitating free-flowing dialogue on a wide range of topics. however, the dialogue's extent in earlier studies is often limited by conversation length, focusing primarily on brief conversations of about 2-15 turns within a single session @xcite @xcite . to support more realistic and extended conversations, a series of studies have explored the role of both external @xcite and internal knowledge @xcite ; what happened, you look unhappy? i fail to select swimming class this semester, but i really want to learn it. don't worry! i can teach you next week. i'm professional! one week ago are you free to learn swimming today? yeah, of course, so what should i do first? let's do some warm-up exercises, the first step is … today guide sum i am careful xu et al., 2022b) on maintaining the feasibility of long-term dialogue. commonly referenced external knowledge, such as commonsense @xcite , medical @xcite , and psychological @xcite knowledge, serves as supplementary guidance for the reasoning process, ensuring logical coherence in extended contexts. in parallel, internal knowledge captured dynamically during long conversations generally contains historical events @xcite @xcite and personas @xcite @xcite . historical events are typically summarized and stored into a memory bank to maintain dialogue coherence across sessions, while interlocutors' personas are maintained via a dynamic persona memory bank, ensuring character consistency in long-term conversations. in this study, we focus on the internal knowledge to integrate dynamically updated historical events and personas to conduct long-term personalized conversations. llm-based autonomous agents. ai agent conception is geared towards autonomous environmental perception, decision-making, and problemsolving capabilities. with the large language models (llms) underlining their impressive generalization potential, leading to their widespread adoption as substitutes for human operators in various research fields @xcite @xcite @xcite . generally, these agents can be categorized into task-oriented agents @xcite @xcite and simulation-oriented agents @xcite @xcite @xcite . task-oriented agents are designed to accurately perform predefined tasks, as seen in applications for web assistance @xcite , game-playing @xcite , and software development @xcite . conversely, simulation-oriented agents are devised to emulate human emotive and cognitive behaviors, having played roles in psychological studies @xcite , social networking platforms @xcite , conflict resolution scenarios @xcite , and recommendation systems @xcite . in addition, recent progress has seen the advent of individuallevel agents that are utilized to simulate specific character behaviors, enhancing the realism and personalization of user-agent interactions @xcite @xcite . this paper falls into simulation-oriented agents to build a human-like open-domain dialogue agent with memory retrieval and character analysis modules. ## method in this section, we introduce the ld-agent in detail with the framework shown in figure 2 . we first introduce the task definition of long-term dialogue in section. 3.1. consequently, we separately introduce the mechanism of event perception (section. 3.2), dynamic personas extraction (section. 3.3), and response generation (section. 3.4). ## conclusion in this work, we delved into the long-term opendomain dialogue agent to meet the real-life chatbot demands for long-term companionship and personalized interactions. unlike most prior solutions, which primarily focus on brief conversations spanning 2-15 turns within a single session, long-term dialogue agents could support consistent interactions over extended periods, even with significant time gaps between sessions. these agents can also perceive and adapt to user personalities, enabling them to deliver more accurate and personalized services. to achieve this, we introduced a general long-term dialogue agent framework, ld-agent, which comprehensively considers both historical events and user-agent personas to support coherent and consistent conversation. its decomposition into three learnable modules significantly enhances its adaptability and transferability. extensive experiments demonstrated ld-agent's strong capability in long-term dialogue tasks, showing its practicality across multiple benchmarks, models, and tasks.