{"slug": "memoryforge-synthesize-lifelong-memory-for-human-like-llm-agents", "title": "MemoryForge: Synthesize Lifelong Memory for Human-Like LLM Agents", "summary": "Researchers introduced MemoryForge, a framework that synthesizes lifelong autobiographical memory from brief target personas to make frozen Large Language Models (LLMs) exhibit more human-like behaviors in role-play and user simulation. In experiments on PersonaGym and SimulatorArena, MemoryForge outperformed strong descriptive conditioning baselines across multiple metrics and LLM backbones, according to the arXiv paper 2608.00007v1.", "body_md": "arXiv:2608.00007v1 Announce Type: new\nAbstract: Equipping Large Language Models (LLMs) with human-like personas is crucial for agentic applications, such as role-play and user simulation. Traditional prompt-based methods rely on descriptive conditioning by injecting static textual profiles, which often makes agents show generic behaviors due to a lack of realistic life memory. To fill this gap, we introduce memory-based conditioning, a paradigm inspired by the cognitive psychology, which replaces abstract profiles with an autobiographical memory base, enabling frozen LLMs to dynamically retrieve situation-relevant memory to guide their behaviors. We formalize its enabling task as customized lifelong memory synthesis and propose MemoryForge, a novel framework to synthesize such lifelong memory from brief target personas. MemoryForge has three key components: a context generator for socio-historical grounding, a life organizer for developmental coherence toward the target identity, and a multi-resolution simulator that balances broad temporal summaries with high-fidelity episodic experiences. Experiments on PersonaGym for role-play and SimulatorArena for user-simulation, show that the synthesized memory base by MemoryForge enables frozen LLMs to exhibit more human-like behaviors than strong descriptive conditioning baselines across multiple metrics and LLM backbones.", "url": "https://wpnews.pro/news/memoryforge-synthesize-lifelong-memory-for-human-like-llm-agents", "canonical_source": "https://arxiv.org/abs/2608.00007", "published_at": "2026-08-04 04:00:00+00:00", "updated_at": "2026-08-04 04:35:22.809181+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-research"], "entities": ["MemoryForge", "arXiv", "PersonaGym", "SimulatorArena", "Large Language Models (LLMs)"], "alternates": {"html": "https://wpnews.pro/news/memoryforge-synthesize-lifelong-memory-for-human-like-llm-agents", "markdown": "https://wpnews.pro/news/memoryforge-synthesize-lifelong-memory-for-human-like-llm-agents.md", "text": "https://wpnews.pro/news/memoryforge-synthesize-lifelong-memory-for-human-like-llm-agents.txt", "jsonld": "https://wpnews.pro/news/memoryforge-synthesize-lifelong-memory-for-human-like-llm-agents.jsonld"}}