Ticket to Ride: Locally Steered Source Routing for the Lightning Network
Bibliographic record
Abstract
Route discovery in the Lightning Network is challenging because senders observe only static channel capacities while real-time balances remain hidden. Existing locally steered schemes such as SpeedyMurmurs protect path privacy but depend on global landmark trees whose maintenance traffic and detours inflate latency and overhead. We present Ticket to Ride (T2R), a locally steered source-routing framework that encodes the set of channels a payment may traverse into a compact ticket - an approximate-membership filter keyed with per-hop Diffie–Hellman secrets. Each relay learns only whether its own outgoing edges are permitted, yielding the same incident-edge privacy as SpeedyMurmurs while eliminating the need to build and maintain global landmark trees or any other shared routing state. Extensive simulations on real snapshots - incorporating churn, silent shutdowns, and random channel saturation - show that T2R boosts end-to-end success by up to 9% and cuts median delay by 1.6× relative to SpeedyMurmurs, all with < 1 kB total overhead and no extra handshakes. Because tickets are processed hop-by-hop and can be prefixed by a trampoline, T2R remains lightweight enough for resource-constrained IoT nodes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".