EP233: Fixing AI memory with backward chaining

Title: Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM SystemsSource: http://arxiv.org/abs/2605.12213v1Summary:This paper presents Goal-Mem, a framework that employs backward chaining and Natural Language Logic to create a goal-oriented reasoning loop for agentic memory systems. It provides a foundational advancement in how agents can systematically decompose complex queries and retrieve missing intermediate facts for robust multi-hop reasoning.