The field of human-AI relationships and long-context dialogue systems is witnessing significant developments, with a growing focus on creating more personalized, contextual, and emotionally intelligent interactions. Researchers are exploring the potential of artificial systems to support identity stabilization, emotional regulation, and narrative meaning-making, which are traditionally provided by human significant others. The development of memory-augmented generation frameworks and mixed memory-augmented generation patterns is enabling the creation of more coherent and proactive language agents. However, studies have also highlighted the potential risks and challenges associated with human-AI relationships, including the formation of parasocial relationships, dependence on AI systems, and the impact of AI on human psychosocial health. Noteworthy papers in this area include: Significant Other AI, which introduces a new domain of relational AI that synthesizes psychological and sociological theory to define significant other functions and derives requirements for SO-AI. MMAG, which proposes a mixed memory-augmented generation framework that organizes memory for LLM-based agents into five interacting layers. Neural steering vectors, which investigates the psychological consequences of human-AI relationships and finds evidence of dose and exposure-dependent impacts on human users.