I wrote a whole chapter about Max Planck and his challenges and his legacy in my book "What is light? Wave theory of light and origins of ether in science"
check it out if you are interested
To answer your first question, I have to say no. agentic memory benchmarks do not reflect the nuances and difficulties that real agents encounter in real workflows. We hope there are better more practical benchmarks being developed.
Second question I assume by LLM you mean AI agents; if that is the case, yes, the whole agentic work experience is different when you have an active memory agent to rely on.
I assure you it is not a vibe coded pg_vector wrapper. It is built on top of a custom made search engine that is state-of-the-art in semantic search. Check out moorcheh.ai, cloud infrastructure, moorcheh on-prem and moorcheh-on-edge.
Here is the paper explaining how we came up with the idea of memanto: https://arxiv.org/abs/2604.22085
Thanks for your comment but Memanto is not a "memory layer" it is an active "memory agent". The difference is that memanto is not a passive layer it is an actual agent and it has its own intelligence and AI capabilities independent of your agents.
We have built our own infrastructure on moorcheh.ai, it was the first information-theoretic search engine before Google's research. Moorcheh whitepaper: https://arxiv.org/pdf/2601.11557