mirror of
https://github.com/lightningnetwork/lnd.git
synced 2026-08-20 13:27:27 +02:00
2b1f78aefc
In this commit, we give the code_drift1 winner the same documentation treatment as the champions: a companion markdown next to its archived source that walks the time machinery -- the 35 minute confidence half-life, the 20 minute bound expiry, and the confidence-weighted interpolation that slides aging evidence back toward the bimodal prior -- and contrasts it axis by axis with lnd's decay, which fades a failure judgment toward the apriori estimate rather than fading trust in evidence toward a learned prior. The doc is honest about why the router is not promoted: it loses every held-out tier, including the drift corpus it was bred on, and its own code carries the flaws of a 400 eval budget. We also flip the champions documentation from pending to resolved. The README's leading caveat no longer says exp-008 is testing whether zero-time-logic is a simulator artifact; it reports that the test ran, that time awareness re-evolved unprompted, and that it lost on every tier, making the champions' timelessness a validated design property at this churn rather than an open question. The results tables gain the drift tier and the drift1 comparison column, the lineage diagram gains its fourth branch, and the per-champion docs get the same surgical pending-to-verdict edits. |
||
|---|---|---|
| .. | ||
| champions | ||
| command-center | ||
| lab | ||
| .gitignore | ||
| codex_lm.py | ||
| evaluate.py | ||
| evaluate_code.py | ||
| export_run.py | ||
| gen_scenarios.py | ||
| preflight.py | ||
| README.md | ||
| refresh_dashboard.sh | ||
| run_gepa.py | ||
| run_gepa_code.py | ||
| run_gepa_omni.py | ||
Routing Optimization Harness
This directory holds the GEPA-based optimization harness for lnd's pathfinding. The core idea: lnd's real routing code (or a candidate replacement algorithm) runs against an in-process simulated Lightning Network with hidden liquidity, an evaluator scores the outcome, and a reflective LLM optimizer (GEPA) proposes improved candidates from the failure feedback.
Components
| Piece | Where | What |
|---|---|---|
| Simulator | routing/sim_*.go |
In-memory LN with hidden balances; real pathfinding + mission control run unmodified against it |
| CLI | cmd/routesim |
params JSON + scenario file in, attempt traces + aggregate JSON out |
| Candidate slot | cmd/routesim/candidate_impl.go |
A complete routing algorithm behind --router=candidate; swapped per candidate via go build -overlay |
| Corpus | gen_scenarios.py |
train/val/test scenario files: topology + liquidity seed + payment batch |
| Evaluators | evaluate.py, evaluate_code.py |
score = success rate − small saturating penalties for attempts and fee ppm |
| Runners | run_gepa.py, run_gepa_code.py |
parameter mode and code mode optimization |
| Reflection LM | codex_lm.py |
GEPA LM protocol via codex exec headless (default gpt-5.6-sol) |
| Lab notebook | lab/ |
running log of experiments, results, ideas |
Quick start
# Build the simulator binary.
go build -o /tmp/routesim ./cmd/routesim
# Generate a scenario corpus.
python3 simulation/gen_scenarios.py --out /tmp/corpus
# Score the lnd defaults on one example.
cd simulation && ROUTESIM_BIN=/tmp/routesim python3 evaluate.py /tmp/corpus/val/example_000.json
# Compare lnd stack vs the candidate router on a scenario file.
/tmp/routesim --scenarios /tmp/corpus/val/example_000.json --router=lnd --traces=false
/tmp/routesim --scenarios /tmp/corpus/val/example_000.json --router=candidate --traces=false
# Full optimization runs. gepa must be installed from git main — a
# durable clone lives at ~/codez/gepa; prefer uv for the env:
# uv venv /tmp/gepa-venv && uv pip install -p /tmp/gepa-venv \
# "~/codez/gepa[full]"
# Also needs the codex CLI authenticated and OPENAI_API_KEY set.
ROUTESIM_BIN=/tmp/routesim python3 run_gepa.py --corpus /tmp/corpus --name run1 --max-evals 400
ROUTESIM_BIN=/tmp/routesim python3 run_gepa_code.py --corpus /tmp/corpus --name code1
The two optimization modes
- Parameter mode (
run_gepa.py) — candidate = JSON of the existing heuristic's knobs (estimator choice, apriori/bimodal params, attempt cost, min probability). Validates the loop and tunes the current paradigm. - Code mode (
run_gepa_code.py) — candidate = the full Go source ofcandidate_impl.go, an entire routing algorithm implementing therouting.SimRouterinterface. This is the paradigm-free path: the candidate sees only gossip, its own balances, and per-attempt feedback. Compile errors are returned to the proposer as feedback.
Anti-reward-hacking measures
- Candidate routers receive a
SimNetworkViewwrapper, not the concrete graph, so hidden balances and liquidity mutation are unreachable. evaluate_code.pyrejects candidates usingunsafe,reflect,os/exec, network packages, etc.- Selection happens on a val split; a sealed test split is only used for final reporting.
- The source's own channels are rebalanced 50/50 before each batch so scores measure routing skill, not sender funding luck.
Command center
command-center/ holds a static dashboard site (serve with
python3 -m http.server from that directory).