How AI and Bitcoin Are Collaborating to Solve a Universal Computer Science Problem
At the intersection of artificial intelligence and decentralized finance, a new advancement is proving how Bitcoin infrastructure can help solve problems far beyond its original scope. We're excited to share that our research, a collaboration between Amboss Technologies and Stillmark, was just published in Algorithms, a peer-reviewed journal. The work introduces Bayesian Binary Search (BBS), a novel machine learning-enhanced algorithm that modernizes a foundational computer science tool: binary search.
What We Built
At its core, BBS is a reimagining of the classic binary search algorithm, widely used in computing for efficient data lookup. Traditional binary search splits the search space in half each time, assuming that each part of the space is equally likely to contain the item you're looking for. That works well when you have no prior knowledge.
But what if you do know something about where the item might be?
That's where BBS comes in. Instead of treating every part of the search space equally, it uses probabilistic models such as Gaussian Processes, Bayesian Neural Networks, or Kernel Density Estimation to estimate where the target is more likely to be. Then, rather than splitting the space down the middle, BBS bisects the space according to the underlying probability distribution. It’s essentially binary search powered by machine learning.
Real-World Application: The Bitcoin Lightning Network
This isn’t just theory. Our research applied BBS to a real-world problem in the Bitcoin Lightning Network: probing payment channels to estimate balances. These balance probes are necessary for optimal routing but are computationally expensive and constrained by network limitations. Traditional binary search is commonly used here, but it treats all amounts as equally likely , a flawed assumption.
By applying BBS, we were able to cut down the number of probes by up to 6% in production, saving 12.5 hours in a single theoretical network assessment. This reduction isn't just an optimization, it directly impacts network efficiency, reduces load, and enhances privacy. Probes on the Lightning Network take time and resources; fewer probes mean faster operations, less network spam, and improved throughput for real payments.
Broader Implications: Beyond Bitcoin
While our initial application is rooted in the Bitcoin Lightning Network, the underlying concept has broad, cross-industry potential:
- Cloud infrastructure: Intelligent file system probing
- Databases: Smarter indexing and query planning
- Scientific computing: Faster root-finding in complex numerical systems
- Healthcare and bioinformatics: Search space reduction for probabilistic models
In each of these domains, data distributions are rarely uniform. Yet most search algorithms still assume they are. BBS challenges that assumption and offers a smarter alternative.
A Bigger Story: Bitcoin and AI Solving Hard Problems
This work tells a bigger story too. Bitcoin, often seen narrowly as a financial tool, is in fact a proving ground for deep technical innovation. By pairing it with modern machine learning techniques, we’re showing how Bitcoin infrastructure can push forward the boundaries of what's possible in computer science.
This research represents an early but important example of Bitcoin and AI collaborating to solve real-world challenges — not just for Bitcoin, but for anyone who needs to search, probe, or optimize in a costly, complex space.
What’s Next?
We believe this is just the beginning. We're continuing to refine BBS and explore its applications beyond Lightning. And we’re especially interested in building bridges with other industries that face similar “expensive search” problems.
If you're working on search, routing, or optimization in your own systems, whether in crypto or otherwise, we’d love to hear from you.
Want to learn more?
The full research paper is open access: Bayesian Binary Search (Algorithms, 2025)
Collaborators: Vikash Singh (Stillmark), Matthew Khanzadeh, Vincent Davis (Amboss), Harrison Rush (Amboss), Emanuele Rossi, Jesse Shrader (Amboss), and Pietro Lio (Cambridge)