Engram

deepseek’s Engram is a technique for large-language-models that introduces a new axis of sparsity through conditional memory via scalable lookup.

Core Problem

Current Transformer architectures waste computation by failing to distinguish between:

• Tasks requiring deep thought (computationally intensive)
• Tasks requiring simple recall (which could be handled via memory lookup)

Key Innovation

Engram implements a scalable lookup mechanism to conditionally access memory for simple recall tasks, reducing unnecessary computation while maintaining performance on complex reasoning tasks.

2026 04 14 DeepSAeek Engram paper Prompt Engineering channel