On January 12, 2026 DeepSeek released its paper on Engram, a novel AI architecture that incorporates conditional memory to optimize how large language models handle information. By utilizing a lookup mechanism for static patterns, this technology separates an AI's logical reasoning from its factual knowledge base. This structural shift allows massive models to run on cheaper hardware by offloading memory requirements to standard host RAM without sacrificing speed. Research indicates that this approach effectively increases model depth, freeing up the system's core processing power for more complex reasoning and long-context tasks. Ultimately, the Engram module enables superior performance across coding, math, and general logic compared to traditional architectures. This innovation suggests a future where AI is significantly more efficient and accessible through the strategic decoupling of memory and computation. Source: https://github.com/deepseek-ai/Engram/blob/main/Engram_paper.pdf