Torrent: Strong, Fast Balance Discovery in the Lightning Network
Bibliographic record
Abstract
The owners of channels in the Bitcoin’s Lightning Network do not disclose their balances in order to protect their privacy, and conceal payment transfers through their channels. Nevertheless, recent studies have shown that channel balances can be discovered using simple probing techniques. These techniques are, however, slow, often rely on specific control messages, and require to open a new channel for every single channel balance discovery or have limited power in discovering balances of remote channels. In this work, we present a powerful balance discovery method called Torrent that overcomes these limitations of existing methods. Unlike the existing techniques, Torrent uses multi-path payments instead of single-path payments. This—together with a novel max flow algorithm designed for the Lightning Network— allows a single probing node to push a large flow of payments through any target channel, making it more likely to discover its balance. Moreover, Torrent can speed-up balance discovery through parallel payment transfers, and pre-computation of payment paths. In addition, Torrent can operate in the absence of routing control messages that are often relied on by the existing channel discovery methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".