Human-Like Lifelong Memory: A Neuroscience-Grounded Architecture for Infinite Interaction
Summary: arXiv:2603.29023v1 Announce Type: cross
Abstract
Large language models (LLMs) have made significant strides in natural language processing; however, they still lack persistent, structured memory for long-term interaction and context-sensitive retrieval. Research indicates that simply expanding context windows is insufficient. In fact, recent evidence suggests that increasing context length alone can degrade reasoning capabilities by up to 85%, even when retrieval mechanisms are optimized. In response to these challenges, we propose a bio-inspired memory framework based on several established theories in neuroscience and psychology.
Introduction
This new architecture is grounded in complementary learning systems theory, cognitive behavioral therapy’s belief hierarchy, dual-process cognition, and fuzzy-trace theory. The proposed framework organizes memory around three core principles, which aim to enhance the interaction capabilities of artificial intelligence systems.
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Memory has valence, not just content:
This principle posits that memory should encompass emotional and associative summaries, referred to as valence vectors. These summaries are arranged in an emergent belief hierarchy inspired by Beck’s cognitive model. This structure allows for instant orientation before engaging in deeper deliberation, thereby improving the efficiency of information retrieval.
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Retrieval defaults to System 1 with System 2 escalation:
The mechanism of retrieval is designed to prioritize automatic spreading activation and passive priming as default processes. Deliberate retrieval is invoked only when necessary, creating a graded approach to epistemic states that addresses potential hallucination in the system’s responses. This aligns with dual-process theory by distinguishing between intuitive and analytical thinking.
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Encoding is active, present, and feedback-dependent:
In this framework, a thalamic gateway plays a crucial role by tagging and routing information between different memory stores. The executive function actively forms gists of information through curiosity-driven exploration rather than passive exposure, enhancing the relevance and accuracy of the knowledge acquired.
Functional Properties
The proposed system incorporates seven functional properties that any implementation must satisfy to ensure effective memory management and retrieval. Over time, the architecture is designed to converge toward System 1 processing, which is analogous to clinical expertise in human cognition. This evolution enables interactions to become progressively cheaper in terms of computational resources and time, rather than more expensive, as the system gains experience.
Conclusion
By integrating principles from neuroscience and psychology, this innovative memory framework aims to overcome the limitations of current large language models. The emphasis on structured, valenced memory, combined with dynamic retrieval and active encoding, promises to enhance the capability of AI systems for meaningful, long-term interactions. As research continues, this architecture may pave the way for more human-like cognitive abilities in artificial intelligence.