Procedural Memory Is Not All You Need: Bridging Cognitive Gaps in LLM-Based Agents
This addresses the problem of LLM-based agents failing in dynamic real-world scenarios, but it is incremental as it builds on existing cognitive theories.
The paper argues that LLMs are limited by their reliance on procedural memory and proposes augmenting them with semantic memory and associative learning to handle complex, unpredictable environments, aiming to bridge the gap to adaptive intelligence.
Large Language Models (LLMs) represent a landmark achievement in Artificial Intelligence (AI), demonstrating unprecedented proficiency in procedural tasks such as text generation, code completion, and conversational coherence. These capabilities stem from their architecture, which mirrors human procedural memory -- the brain's ability to automate repetitive, pattern-driven tasks through practice. However, as LLMs are increasingly deployed in real-world applications, it becomes impossible to ignore their limitations operating in complex, unpredictable environments. This paper argues that LLMs, while transformative, are fundamentally constrained by their reliance on procedural memory. To create agents capable of navigating ``wicked'' learning environments -- where rules shift, feedback is ambiguous, and novelty is the norm -- we must augment LLMs with semantic memory and associative learning systems. By adopting a modular architecture that decouples these cognitive functions, we can bridge the gap between narrow procedural expertise and the adaptive intelligence required for real-world problem-solving.