Brain-Inspired Memory for Large Language Models
The article discusses a long-term memory system called nan-forget, built for AI coding tools like Claude Code, that takes inspiration from how the human brain handles memory.
Why it matters
This work demonstrates how principles from human memory can be applied to improve long-term memory systems for AI assistants and language models.
Key Points
- 1Applies the forgetting curve to memories, letting unused ones fade over time
- 2Uses spreading activation to retrieve related memories beyond the initial search query
- 3Consolidates fragmented memories into coherent knowledge during sleep
Details
The author noticed that when using Claude Code, they were repeatedly explaining the same information, as the language model would suggest approaches they had already rejected. Existing memory tools treat memory as a database problem, but the brain works differently. The author built nan-forget, which takes three key ideas from neuroscience: the forgetting curve, spreading activation, and sleep consolidation. Nan-forget applies an exponential decay to memories based on time since last access, and boosts frequently accessed memories. It also has a multi-stage retrieval process, going from recognition to full recall and then spreading activation to surface related context. Finally, it consolidates fragmented memories on a regular basis, summarizing clusters of similar entries into a single coherent memory. This is all done deterministically, without any LLM calls or API costs.
No comments yet
Be the first to comment