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9c07cbe7f6
In this commit, we land exp-021's instrument: two flag-gated changes to lnd's own payment stack, byte-identical to stock with both flags off (proven against a pre-change binary across four tiers). soft_unknown is the live half. An unreadable failure now penalizes exactly one pair (the lowest-probability hop at the attempt amount) instead of every pair of the route in both directions, which is the mechanism exp-019 showed spiraling lnd into give-ups from a 10% unreadable-error rate. The smoke shows the intended signature: with the flag on, lnd's trajectory is invariant to the unreadable rate. adaptive_split is the measured negative, kept as instrumentation. Three designs were built and each reduced to the same thing: descend geometrically from the learned bound, which lnd's blind halving already does at the fastest ratio of any variant, for free. The supremum search paid a wire attempt per linear step; the backoff variant re-derived halving with a slower constant; the expected-value ladder degenerates to its top rung under apriori's flat belief and cannot escape the retry loop bimodal pins itself into. The conclusion the writeup carries: the reactive split-retry control flow is not where the evolved routers' edge lives, so the distillation question moves to plan-time mechanisms. |
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|---|---|---|
| .. | ||
| champions | ||
| command-center | ||
| lab | ||
| .gitignore | ||
| claude_lm.py | ||
| codex_lm.py | ||
| evaluate.py | ||
| evaluate_code.py | ||
| export_run.py | ||
| gen_family_corpora.py | ||
| gen_mainnet_scenarios.py | ||
| gen_mainnet_variants.py | ||
| gen_scenarios.py | ||
| gen_served_weights.py | ||
| gen_warmup_scenarios.py | ||
| params_lnd_bimodal.json | ||
| params_lnd_no_contagion.json | ||
| params_lnd_patch.json | ||
| preflight.py | ||
| README.md | ||
| refresh_dashboard.sh | ||
| run_gepa.py | ||
| run_gepa_code.py | ||
| run_gepa_omni.py | ||
| sweep_validate.py | ||
| warmup_curve.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).