docs: record Rain EP bias evidence

1 files changed, 28 insertions, 5 deletions

diff --git a/TWO_STATE_BIAS_PROGRAM.md b/TWO_STATE_BIAS_PROGRAM.md
index 6811739..c8ed934 100644
--- a/TWO_STATE_BIAS_PROGRAM.md
+++ b/TWO_STATE_BIAS_PROGRAM.md
@@ -317,10 +317,33 @@ Stop this paper direction if:
   adapter and causal correction but is not independent real-world evidence.
 - EP adapter: implemented against the pinned Rain author code with manual local
   EP parameter gradients, autograd disabled for SDIL, local neutral LMS, replay
-  checks and clean/raw/constant/innovation/oracle/noise endpoints. CUDA
-  performance screening is active; no task-performance claim is recorded yet.
+  checks and clean/raw/constant/innovation/oracle/noise endpoints. The first
+  parameter-measurement adapter is closed: fixed bias makes raw EP nonfinite
+  while same-RMS noise remains trainable, but online, 64-batch and matched
+  1000-batch affine calibration all fail to recover clean learning; see
+  `results/ep_bias/s0_summary.json`.
+- EP neuron-state adapter: mechanism-positive but not yet confirmed. The S1
+  development screen reaches `78.05/49.30/62.95/77.10/78.10%` for
+  clean/raw/constant/innovation/oracle with 128 matched neutral observations
+  and no extra equilibrium, but its frozen five-seed C1 gate fails. Innovation
+  beats raw in every seed by `43.14` points on average (95% lower bound
+  `24.35`), and its residual is below constant in every seed, but one seed
+  reverses the accuracy comparison against constant; clean/oracle/noise endpoint
+  bounds also fail under the unstable three-epoch Rain dynamics. See
+  `results/ep_bias/c1_gate.json`.
+- A more hardware-grounded S3 bias is normalized only by the initial free-state
+  activity and is bitwise independent of beta sign and task difference. In its
+  single-seed development screen, clean/raw/constant/innovation reach
+  `79.15/62.75/67.10/77.75%`; innovation is `+15.00/+10.65` points over
+  raw/constant and is `1.40` points below clean with essentially zero measured
+  wall overhead. This is not a confirmation. Random-sign beta is not yet a
+  functioning comparator in the pinned positive-EP configuration: both random
+  sign and fixed negative beta remain at `9.9%` even with zero injected bias,
+  so negative-beta stability must be solved independently before comparison;
+  see `results/ep_bias/s3_summary.json`.
 - Coupled-learning/DCHN adapter: not implemented under this bias model.
 - Current score for this new paper framing: 5/10. Passing the artificial Dual
-  Prop confirmation does not repair the failed physical strong-baseline gate;
-  promotion requires an independently grounded bias source and a passed
-  cross-backbone confirmation.
+  Prop confirmation and the single-seed Rain S3 screen do not repair the failed
+  physical strong-baseline gate or the failed Rain C1 confirmation. Promotion
+  requires an independently grounded bias source, a stable EP protocol with a
+  functioning random-sign comparator, and a passed cross-backbone confirmation.