lnd/simulation

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 (needs pip install "gepa[full]" from git main,
# the codex CLI authenticated, and OPENAI_API_KEY for LiteLLM fallback).
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

1. 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.
2. Code mode (run_gepa_code.py) — candidate = the full Go source of candidate_impl.go, an entire routing algorithm implementing the routing.SimRouter interface. 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 SimNetworkView wrapper, not the concrete graph, so hidden balances and liquidity mutation are unreachable.
• evaluate_code.py rejects candidates using unsafe, 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).