LLM代理如何像人类一样管理程序性记忆？这篇论文提出控制、适应与评估的新框架。
arXiv:2606.23127v1 Announce Type: new Abstract: Procedural memory is increasingly used to improve LLM agents on recurring workplace tasks, yet its abi…
指出主流AI认知架构缺失知识层，导致事实与经验混淆，引发认知错误
arXiv:2604.11364v2 Announce Type: replace Abstract: The two most influential cognitive architecture frameworks for AI agents, CoALA [21] and JEPA [12]…
这篇论文提出“海马体显式记忆”是LLM迈向通用人工智能的关键，类比人类记忆机制，观点新颖有启发性。
arXiv:2606.11245v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, raising e…
LLM并非AGI死胡同，关键在于构建System-2协调层来驾驭模式库。
arXiv:2512.05765v2 Announce Type: replace Abstract: In this paper we argue that influential critiques dismissing Large Language Models (LLMs) as a dea…
LLM面临推理天花板，世界模型或成通往AGI的关键路径，这篇论文系统对比了两者在认知架构上的本质差异。
arXiv:2605.23972v1 Announce Type: new Abstract: Large language models achieve strong performance in language generation and knowledge-intensive tasks,…
PAVE架构让AI代理学会在规则社会中“合法越轨”，模拟更真实的人类社会行为。
arXiv:2605.19351v1 Announce Type: cross Abstract: Generative agents based on large language models reproduce believable human behavior in cooperative …