Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems

ArXi:2605.12213v1 Announce Type: new LLM-based conversational AI agents struggle to maintain coherent behavior over long horizons due to limited context. While RAG-based approaches are increasingly adopted to overcome this limitation by storing interactions in external memory modules and performing retrieval from them, their effectiveness in answering challenging questions (e.g., multi-hop, commonsense) ultimately depends on the agent's ability to reason over the retrieved information.