Wolfgang Minker

Large language models (LLMs) have advanced the field of artificial intelligence (AI) and are a powerful enabler for interactive systems. However, they still face challenges in long-term interactions that require adaptation towards the user as well as contextual knowledge and an understanding of the ever-changing environment. To overcome these challenges, holistic memory modeling is required to efficiently retrieve and store relevant information across sessions for accurate responses. Cognitive AI, which aims to simulate the human thought process in a computerized model, highlights interesting aspects, such as thoughts, memory mechanisms, and decision making, that can contribute towards improved memory modeling for LLMs. Inspired by these principles, we propose CAIM, a cognitive AI memory framework that models key aspects of human memory through a multi-agent architecture. Specialized LLM-based agents handle memory-related functions such as retrieval, contextual relevance evaluation, and memory maintenance. We compare CAIM against existing approaches, focusing on metrics such as retrieval accuracy, response correctness, and contextual coherence. The results demonstrate that CAIM outperforms baseline frameworks in different metrics, highlighting its context awareness and demonstrating its contribution to memory mechanisms for improving long-term human-AI interactions.

Large language models (LLMs) increasingly serve as the central control unit of AI agents, yet current approaches remain limited in their ability to deliver personalized interactions. While Retrieval Augmented Generation enhances LLM capabilities by improving context-awareness, it lacks mechanisms to combine contextual information with user-specific data. Although personalization has been studied in fields such as human-computer interaction or cognitive science, existing perspectives largely remain conceptual, with limited focus on technical implementation. To address these gaps, we build on a unified definition of personalization as a conceptual foundation to derive technical requirements for adaptive, user-centered LLM-based agents. Combined with established agentic AI patterns such as multi-agent collaboration or multi-source retrieval, we present a framework that integrates persistent memory, dynamic coordination, self-validation, and evolving user profiles to enable personalized long-term interactions. We evaluate our approach on three public datasets using metrics such as retrieval accuracy, response correctness, or BertScore. We complement these results with a five-day pilot user study providing initial insights into user feedback on perceived personalization. The study provides early indications that guide future work and highlights the potential of integrating persistent memory and user profiles to improve the adaptivity and perceived personalization of LLM-based agents.

This paper investigates the evolving landscape of Multi-User Conversational Interfaces, addressing the limitations of traditional systems that primarily focus on single-user interactions. As real-world applications grow more complex, there is a pressing need for conversational systems capable of facilitating dialogues among multiple participants. Our systematic survey reviews recent advancements in the field, highlighting innovative architectures, application domains, user and dialogue modelling, and evaluation metrics tailored for multi-user contexts. We conduct a comprehensive analysis of relevant literature, employing both quantitative and qualitative methodologies to identify common patterns and challenges in multi-user interactions. The findings underscore the importance of developing robust interfaces that can effectively manage overlapping dialogues, ensure collaborative group work, and enhance overall conversational quality. This work contributes to the understanding of Multi-User Conversational Interfaces and may help as a basis for future research aiming to develop more natural, user-friendly, and effective conversational interfaces.

Large language models (LLMs) have advanced the field of artificial intelligence (AI) and are a powerful enabler for interactive systems. However, they still face challenges in long-term interactions that require adaptation towards the user as well as contextual knowledge and understanding of the ever-changing environment. To overcome these challenges, holistic memory modeling is required to efficiently retrieve and store relevant information across interaction sessions for suitable responses. Cognitive AI, which aims to simulate the human thought process in a computerized model, highlights interesting aspects, such as thoughts, memory mechanisms, and decision-making, that can contribute towards improved memory modeling for LLMs. Inspired by these cognitive AI principles, we propose our memory framework CAIM. CAIM consists of three modules: 1.) The Memory Controller as the central decision unit; 2.) the Memory Retrieval, which filters relevant data for interaction upon request; and 3.) the Post-Thinking, which maintains the memory storage. We compare CAIM against existing approaches, focusing on metrics such as retrieval accuracy, response correctness, contextual coherence, and memory storage. The results demonstrate that CAIM outperforms baseline frameworks across different metrics, highlighting its context-awareness and potential to improve long-term human-AI interactions.