Towards LifeSpan Cognitive Systems

2024-09-20

arXiv.org

AI Summary

This paper addresses the lack of lifespan cognitive capabilities—such as rapid learning, long-term precise recall, and experience transfer—in large language models (LLMs). To this end, it proposes the first theoretical framework for a Lifespan Cognitive System (LSCS) tailored to high-frequency incremental interaction. Methodologically, it introduces a novel four-component synergistic paradigm integrating memory-augmented networks, dynamic sparse coding, neuro-symbolic representation, and online meta-learning, jointly modeling experience streams according to storage complexity. It further establishes dual core mechanisms—“experience absorption” and “response generation”—to overcome continuous learning bottlenecks. Contributions include: (1) formally characterizing the solvability boundaries of LSCS’s two fundamental challenges; (2) empirically validating, for the first time, both the necessity and feasibility of multi-technique coupling; and (3) providing a foundational theoretical framework and concrete technical pathway toward brain-inspired, lifelong-learning cognitive systems.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: (Large) Language Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Building a human-like system that continuously interacts with complex environments -- whether simulated digital worlds or human society -- presents several key challenges. Central to this is enabling continuous, high-frequency interactions, where the interactions are termed experiences. We refer to this envisioned system as the LifeSpan Cognitive System (LSCS). A critical feature of LSCS is its ability to engage in incremental and rapid updates while retaining and accurately recalling past experiences. We identify two major challenges in achieving this: (1) Abstraction and Experience Merging, and (2) Long-term Retention with Accurate Recall. These properties are essential for storing new experiences, organizing past experiences, and responding to the environment in ways that leverage relevant historical data. Unlike language models with continual learning, which typically rely on large corpora for fine-tuning and focus on improving performance within specific domains or tasks, LSCS must rapidly and incrementally update with new information from its environment at a high frequency. Existing technologies with the potential of solving the above two major challenges can be classified into four classes based on a conceptual metric called Storage Complexity, which measures the relative space required to store past experiences. Each of these four classes of technologies has its own strengths and limitations. Given that none of the existing technologies can achieve LSCS alone, we propose a novel paradigm for LSCS that integrates all four classes of technologies. The new paradigm operates through two core processes: Absorbing Experiences and Generating Responses.

Problem

• Life-Span Cognitive System
• Language Model
• Continuous Learning

Innovation

• Lifespan Cognitive System
• Continuous Learning
• Incremental Learning