LLMs are so well tuned for optimizing the lighting pathfinding problem. Give them a dataset and tell them to optimize a parameter and they go ham.
Big things ahead for the LDK pathfinder and lighting payment success rates.
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Does this mean that llms are also well-tuned to figuring out the likely path of a payment?
That reliability might come with a price.
they are a traveling salesman at heart
Not directly, no. But if you have a large dataset to build an algorithm from…..
what is your dataset?
The same old probing dataset we used to optimize the scoring analysis last time, but with a year and a half more data, a bugfix in the harness, and LLMs trying several totally different approaches to investigate potential directions.
After I land some updates maybe I’ll put up a website and let people submit their results and throw their agents at the problem.
Sadly it really wants to over-tune for nodes that send a lot of probes, which isn’t really ideal, but may be able to push it towards having options for everyone.
That is absurd.
More reliability at the cost of more/better surveillance.
How so?
They would know your every hop, privacy concern?