Human-Like Lifelong Memory: A Neuroscience-Grounded Architecture for Infinite Interaction

TL;DR

This paper introduces a neuroscience-inspired memory architecture for large language models that enhances long-term, context-sensitive interaction by mimicking human memory principles and cognitive processes.

Contribution

It proposes a novel, bio-inspired memory framework grounded in multiple cognitive theories, enabling more efficient and human-like long-term interaction in language models.

Findings

Memory with valence enables instant orientation before deliberation

System 1 default retrieval with System 2 escalation improves reasoning

Converging toward System 1 processing reduces interaction costs over time

Abstract

Large language models lack persistent, structured memory for long-term interaction and context-sensitive retrieval. Expanding context windows does not solve this: recent evidence shows that context length alone degrades reasoning by up to 85% - even with perfect retrieval. We propose a bio-inspired memory framework grounded in complementary learning systems theory, cognitive behavioral therapy's belief hierarchy, dual-process cognition, and fuzzy-trace theory, organized around three principles: (1) Memory has valence, not just content - pre-computed emotional-associative summaries (valence vectors) organized in an emergent belief hierarchy inspired by Beck's cognitive model enable instant orientation before deliberation; (2) Retrieval defaults to System 1 with System 2 escalation - automatic spreading activation and passive priming as default, with deliberate retrieval only when needed, and graded epistemic states that address hallucination structurally; and (3) Encoding is active, present, and feedback-dependent - a thalamic gateway tags and routes information between stores, while the executive forms gists through curiosity-driven investigation, not passive exposure. Seven functional properties specify what any implementation must satisfy. Over time, the system converges toward System 1 processing - the computational analog of clinical expertise - producing interactions that become cheaper, not more expensive, with experience.