diff --git a/CLAUDE.md b/CLAUDE.md
index 9b6b9d31e..39aa36945 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -25,10 +25,19 @@ writeups live in `simulation/lab/experiments/` (exp-001…exp-009).
 - Parameter tuning alone could NOT beat lnd's defaults (exp-002): the
   paradigm is the lever, not the knobs.
 - What the champions evolved (all pure Go, `simulation/champions/`):
-  dropped mission control and ALL time-decay; rediscovered the bimodal
-  liquidity prior from failure traces; invented per-directed-channel
-  liquidity intervals (lowerOK/upperFail bounds + evidence-count
-  confidence) — that's where the 8.6× attempt reduction comes from.
+  dropped mission control and ALL time-decay; invented
+  per-directed-channel liquidity intervals (lowerOK/upperFail bounds +
+  evidence-count confidence) — that's where the 8.6× attempt reduction
+  comes from. **Correction (WHY.md §0):** we long claimed they
+  "rediscovered the bimodal prior from failure traces." They did not —
+  the harness prompt has stated the bimodal hypothesis since the first
+  committed version. What was NOT supplied: the prior's functional
+  shape and constants, and the entire interval apparatus. Worse, the
+  evolved constants FIT OUR GENERATOR (`sim_liquidity.go` draws
+  `ExpFloat64()*0.05`; atomic1's low mode is `exp(−x/0.055)`), and the
+  mainnet tier overwrites real balances with that same generator — so
+  the mainnet number is real topology and policies, synthetic
+  liquidity. Fixing that is the top pre-upstream task.
 
 ## Map
 
diff --git a/simulation/lab/NOTEBOOK.md b/simulation/lab/NOTEBOOK.md
index 7cf7328fd..852665a83 100644
--- a/simulation/lab/NOTEBOOK.md
+++ b/simulation/lab/NOTEBOOK.md
@@ -78,10 +78,14 @@ drowned success-rate deltas; penalties now saturate at −0.25 total.
 - OOD corpus-v2 test: hb1 0.545 > seed 0.487 > lnd 0.357
 - ~9 attempts/payment vs lnd's ~50, higher success on both.
 
-What it discovered from failure traces alone: an explicit **bimodal
-liquidity prior** (rediscovering lnd's own bimodal-estimator hypothesis)
-+ per-edge liquidity bounds with confidence + risk-adjusted Dijkstra.
-Clean (no exploit). Full detail + caveats: exp-006. Champion saved to
+What it built: an explicit **bimodal liquidity prior** + per-edge
+liquidity bounds with confidence + risk-adjusted Dijkstra. Clean (no
+exploit). **Correction (2026-07-26, WHY.md §0):** this entry
+originally read "discovered from failure traces alone." That is
+wrong — the harness prompt has stated the bimodal hypothesis under
+"environment truths" since the first committed version. The prior's
+shape and constants and the whole interval apparatus were the run's
+own work; the hypothesis was handed to it. Full detail + caveats: exp-006. Champion saved to
 `champions/router_hb1_v1.go`. Sim audit (exp-005) fixed a critical
 sandbox escape before it was exploited.
 
diff --git a/simulation/lab/experiments/exp-006-breakthrough.md b/simulation/lab/experiments/exp-006-breakthrough.md
index 1c01485ba..94d89caac 100644
--- a/simulation/lab/experiments/exp-006-breakthrough.md
+++ b/simulation/lab/experiments/exp-006-breakthrough.md
@@ -42,8 +42,21 @@ and per-attempt failure traces:
      amount approaches capacity,
    - floored/capped to [0.005, 0.985].
    This is the same "funds sit at one end of the channel" hypothesis
-   lnd's bimodal estimator was analytically derived from — reinvented by
-   the LLM from failure feedback alone.
+   lnd's bimodal estimator was analytically derived from.
+
+   **Correction (2026-07-26, WHY.md §0):** this bullet originally
+   ended "reinvented by the LLM from failure feedback alone," and that
+   is false. The harness prompt states the bimodal hypothesis verbatim
+   under "environment truths worth exploiting," and has since the
+   first committed version of `run_gepa_code.py`. What the run
+   actually produced is the functional form (exponential low mode plus
+   logistic cliff), every constant in it, and the interval machinery
+   in the next bullet — real work, but not the discovery we claimed.
+   Sharper still: `0.025` here and `0.018` in mx_c3 sit close to the
+   `ExpFloat64()*0.05` that `sim_liquidity.go` generates with, so the
+   prior is fitted to our generator. Until a corpus carries liquidity
+   from some other source, treat the prior as calibration and the
+   intervals as the finding.
 
 2. **Per-edge liquidity bounds with confidence.** `edgeProbability`
    tracks `lowerOK` (largest amount known to pass), `upperFail` (smallest