AMBOSS RESEARCH
We publish machine learning research on Bitcoin and the Lightning Network, then ship it. Explore the papers behind our work on routing, liquidity, and network intelligence.
Published papers
Liquidity allocated in production
Channel opens executed by our RL agent
Probing efficiency gain in production
THE WORK BEHIND THE TECHNOLOGY
3 papers · Research & applied results
We address liquidity placement in the Bitcoin Lightning Network (LN): given a fixed budget, which channels should a node open to maximize its routing capacity?
Deployed in production for peer recommendations: 4,640 channel-open decisions allocating 267.3 BTC (over $16 million) across 30 managed nodes.
We present Bayesian Binary Search (BBS), a novel framework that bridges statistical learning theory/probabilistic machine learning and binary search.
In production for Lightning channel balance probing with a 3 to 6% efficiency gain. Published in the peer-reviewed journal Algorithms, in collaboration with Stillmark.
This research explores the feasibility of using machine learning models to interpolate channel balances within the network, which can be used for optimizing the network's pathfinding algorithms.
Predicts hidden channel balances from node and channel features alone, outperforming the equal-split baseline by 10%.
FROM PAPER TO PRODUCT
Our research informs the infrastructure behind Amboss. Discover the payment technology it helps us build.