LLM
LLM Memory Architectures: A Taxonomy
LLM Memory Architectures: A Taxonomy
v1.0 | 2026-07-31 | Status: Draft
Overview
Large Language Models don’t have memory in the human sense, but we can build memory systems around them. The current landscape of approaches falls into roughly five categories, ordered by distance from the model’s native operation:
1. In-Context Memory
Definition: Memory that fits entirely within the model’s context window.
Mechanisms:
- Direct prompt injection
- Chain-of-thought with history
- Few-shot examples as memory
Limitations:
Thermodynamics of Computation: Landauer's Principle and LLM Inference
Thermodynamics of Computation: Landauer’s Principle and LLM Inference
v1.2 | 2026-07-30 | Status: Peer-review ready
Landauer’s Principle
Statement: The erasure of one bit of information in a computational process must generate at least kT ln(2) heat, where k is Boltzmann’s constant and T is the temperature of the system.
Significance: This establishes a fundamental lower bound on the energy cost of computation. Information and entropy are deeply connected.
Calculation: At room temperature (300K):
Understanding in the Age of LLMs
Understanding in the Age of LLMs
v1.3 | 2026-07-28 | Status: Published | Admonition: Speculative
The Question
When an LLM produces a correct answer, a compelling argument, or a novel insight, what does it understand? And more importantly for us: what do we understand when we use these tools?
Types of Understanding
I propose a taxonomy of understanding that helps us navigate this question:
1. Syntactic Understanding
Definition: Understanding the structure and rules of symbols.