Really fascinating exploration of cognitive memory models applied to AI systems. The forgetting curve concept is particularly relevant right now — most LLM context window approaches treat all tokens equally, but human memory is inherently selective. The idea of importance-weighted retention could be huge for multi-agent architectures where agents need to share and prioritize knowledge across long-running tasks. Have you looked into how retrieval-augmented generation compares to the memory management approach you're describing here?
Thank you!
RAG mainly helps retrieve relevant information, while our approach focuses more on what should be remembered, reinforced, or forgotten over time. I think combining RAG with Ebbinghaus-based memory decay could make long-running multi-agent systems much more efficient and human-like.
Archit Mittal
I Automate Chaos — AI workflows, n8n, Claude, and open-source automation for businesses. Turning repetitive work into one-click systems.
Really fascinating exploration of cognitive memory models applied to AI systems. The forgetting curve concept is particularly relevant right now — most LLM context window approaches treat all tokens equally, but human memory is inherently selective. The idea of importance-weighted retention could be huge for multi-agent architectures where agents need to share and prioritize knowledge across long-running tasks. Have you looked into how retrieval-augmented generation compares to the memory management approach you're describing here?