paper: integrate calibrated oral-B-v2 evidence

1 files changed, 125 insertions, 25 deletions

diff --git a/paper/MANUSCRIPT.md b/paper/MANUSCRIPT.md
index b4b735a..733284e 100644
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@@ -29,13 +29,16 @@ dynamic paired-neutral innovation rule reaches 91.584% mean CIFAR-10 test
 accuracy across five untouched seeds, versus 91.388% for clean reciprocal
 credit; the one-sided 95% upper bound on its deficit is 0.131 points. It uses
 zero task-loss queries and 1.326 times the matched BP MAC estimate, but pays for
-one instruction-off neutral observation per training example. A preregistered
-synthetic BCI confirmation learns the task and causal sign yet fails outcome
-vectorization and longitudinal prediction. The supported conclusion is
-therefore algorithmic and narrow: neutral somato-dendritic innovation can
-protect local credit from soma-predictable traffic and remain stable on
-ResNet-20, without establishing a cortical learning rule or positive utility
-from added standard-network depth.
+one instruction-off neutral observation per training example. In a separate
+six-task by five-model synthetic BCI confirmation, a local actor--critic
+innovation reaches 100.000% task success, 99.833% terminal outcome decoding,
+and 30/30 predicted causal-role signs. An acute outcome lesion reduces
+role-aligned separation by 0.400. The supported conclusion remains
+algorithmic: somato-dendritic innovation can protect local credit from
+soma-predictable traffic, remain stable on ResNet-20, and multiplex performance
+change with outcome surprise in a controlled dynamical task. These results do
+not establish a cortical learning rule or positive utility from added
+standard-network depth.
 
 ## 1. Introduction
 
@@ -86,10 +89,9 @@ Our contributions are:
 3. frozen evidence that the innovation operation is load-bearing under
    soma-predictable traffic, including an independently confirmed ResNet-20
    endpoint; and
-4. retained negative results showing where the proposal does not work:
-   arbitrary top-down traffic, an unstable static ResNet predictor, a failed
-   desired-velocity/online-control interpretation, and an untouched
-   population-signature confirmation that fails its joint gate.
+4. an independently confirmed local actor--critic instantiation in which
+   learned causal roles vectorize performance change and outcome surprise,
+   together with retained failed protocols that delimit the result.
 
 ## 2. Somato-dendritic innovation learning
 
@@ -196,6 +198,37 @@ loss, downstream weight, or reverse pass. It is nevertheless a real
 instruction-off microphase: every neutral observation and its elementwise
 arithmetic are counted, and we do not call this variant single-phase.
 
+### 2.4 Temporal-difference outcome innovation
+
+The synthetic BCI branch uses the same innovation variable in a continuous
+dynamical task. A local linear critic reads the surrounding somatic population,
+
+\[
+V_t=v^\mathsf{T}[1,h_t],
+\qquad
+\delta_t=\rho_t+\gamma(1-z_t)V_{t+1}-V_t,
+\]
+
+where \(z_t\) marks a terminal event and
+\(\rho_t=|e_{t-1}|-|e_t|+\mathbb{1}\{\text{rewarded terminal}\}\).
+Sparse antithetic cursor probes estimate each cell's signed causal role
+\(m_i=\partial z/\partial h_i\); the instructional apical term is
+\(m_i\delta_t\). A local temporal eligibility trace,
+
+\[
+E_{ij,t}=0.8E_{ij,t-1}+
+(1-h_{i,t}^2)x_{j,t},
+\qquad
+\Delta W_{ij,t}=\eta r_{i,t}E_{ij,t},
+\]
+
+assigns the innovation to recent synaptic activity. Actor, critic, role
+estimator, and neutral predictor use manual local updates without autograd or
+task-loss queries. The terminal reward is explicitly supplied, so outcome
+encoding alone is not evidence for an emergent error code; the causal tests
+are residualization, learned role vectorization, critic expectedness, acute
+lesions, and untouched replication.
+
 ## 3. What residualization guarantees—and what it does not
 
 Let \(n\) be neutral apical traffic and let
@@ -279,6 +312,25 @@ and dynamic innovation from scratch for untouched seeds 10--14, uses no
 validation examples, and evaluates the 10,000-example test set once at the
 endpoint. Both conditions use the same initialization/data seed pairing.
 
+### Synthetic BCI confirmation
+
+The biological-signature test uses 40 cells with known experimenter-only
+causal roles, 14 training days, 64 episodes per day, and 28 maximum steps per
+episode. Every condition receives the same instruction-off neutral warmup and
+paired scalar cursor probes. Fixed-role, plasticity-lesion, critic-lesion,
+outcome-lesion, and exact-role diagnostic conditions share all exogenous
+trajectories.
+
+Because independently learned policies have different cursor scales, a fixed
+absolute target produced a disclosed ceiling failure. The final assay freezes
+the algorithm, runs 512 separate outcome-free calibration trajectories, and
+uses fixed cursor-maximum quantiles
+\(\{0.20,0.35,0.50,0.65,0.80\}\) as target levels. Rewarded and timeout
+outcomes are then measured on independent trajectories; no evaluation label
+selects or reweights a target. Confirmation crosses six untouched task seeds
+and five model seeds. Uncertainty first averages models within task and then
+uses the six task seeds as independent clusters.
+
 ### Baselines and cost
 
 In-repository controls include BP, FA, DFA, direct node perturbation,
@@ -371,7 +423,7 @@ time is 1.47 times paired clean KP. The result supports ResNet-20 robustness
 under the audited traffic intervention. It does not support a ResNet-20-to-56
 depth claim.
 
-## 6. Biological-signature test and negative evidence
+## 6. Biological-signature test and outcome-surprise evidence
 
 The original online-control/desired-velocity screen fails its frozen causal
 sign and acute-control gates. We therefore constructed a separate
@@ -395,14 +447,59 @@ the broader population outcome-vectorization and longitudinal signatures.
 Because online control is disabled in this recovery, even a passed plasticity
 gate would not establish an online desired-velocity controller.
 
+That failure exposed two structural mismatches rather than motivating a
+threshold repair. First, the old trajectory terminated without an explicit
+reward/timeout event, so the residual was never asked to encode the variable
+used by its outcome decoder. Second, near-ceiling task success left almost no
+unrewarded trials. A separately frozen v2 added an explicit actor--critic
+outcome phase. Its first development grid failed from a cold start when dense
+performance velocity was scaled to one quarter of the previously validated
+signal. Restoring unit scale recovered learning, but a fixed target ladder
+failed one new development seed because policy output scale varied. Both
+failures remain in the repository.
+
+The final recovery changes no learning parameter after that diagnosis. It uses
+the label-free calibration split described above and then opens a fully
+untouched 30-record confirmation.
+
+![Local actor--critic learning, residualization, psychometric calibration, and outcome lesions.](../results/figs/figure5_bci_v2.png)
+
+**Figure 5: Role-vectorized temporal-difference outcome surprise.** The
+renderer reads all 30 confirmation records and uses the task seed, not each
+model, as the uncertainty unit.
+
+All six task clusters reach 100.000% final success; mean learning gain is
+98.802 points and the fixed-role gap is 99.974 points. Learned-role cosine is
+0.9761. Nonterminal residual--soma correlation is 0.063, while subtracting the
+neutral prediction reduces absolute correlation by 0.936. The preceding
+surrounding population predicts causal-cell residual sign at 54.37% balanced
+accuracy, with a one-sided lower bound of 54.26%, and velocity has a 0.638
+absolute cross-validated correlation advantage over error magnitude. All 30
+records have the predicted positive P+/P- sign.
+
+Independent calibrated trials are balanced at 50.094% rewarded outcomes.
+Terminal residual outcome accuracy is 99.833% with a 99.680% lower bound,
+versus 73.50% from pre-outcome soma. Acute removal of terminal outcome input
+reduces role-aligned separation by 0.400 (lower bound 0.389). The learned
+critic contributes 0.319 of expectedness modulation (lower bound 0.281), and
+that contribution is paired with its stored value prediction.
+
+The result establishes the claimed signal within this synthetic paradigm:
+ordinary soma coupling is subtracted, recent performance change and terminal
+outcome are vectorized by learned cell-specific causal roles, the residual
+drives local eligibility-based plasticity, and the critic converts raw outcome
+into surprise. It does not establish that the same plasticity rule operates in
+cortex. Calibration makes rewarded and timeout trials statistically
+identifiable; it is an explicit psychometric phase and not a biological
+prediction by itself.
+
 These negatives constrain interpretation:
 
 - we do not infer that dendritic residuals directly drive cortical plasticity;
 - we do not infer that cortex implements BP;
 - we do not claim arbitrary top-down traffic removal;
 - we do not claim positive utility from adding standard ResNet depth; and
-- we do not relabel task learning and sign inversion as a passed population
-  signature.
+- we do not treat directly supplied terminal reward as an emergent error.
 
 ## 7. Related work
 
@@ -462,17 +559,19 @@ MAC overhead. Hardware implementations may price local elementwise operations,
 state storage, and phases differently from GPUs; this is why we report several
 resource axes rather than one scalar cost.
 
-The strongest standard result uses only ResNet-20. The separately frozen
-ResNet-20/32/56 panel remains unopened because its biological prerequisite
-failed. This preserves the declared accept-to-oral ordering but leaves positive
-added-depth utility unresolved.
+The strongest standard result uses only ResNet-20. The old separately frozen
+ResNet-20/32/56 panel remains unopened because its original biological
+prerequisite failed. The successful v2 gate permits only a new independently
+frozen depth protocol; it cannot retroactively open the old panel. Positive
+added-depth utility therefore remains unresolved.
 
-Finally, the synthetic BCI negatives are scientifically important. The
-residual operation can be algorithmically useful without reproducing every
-signature of cortical dendrites. A stronger biological paper needs a new
-prediction and mechanism frozen independently of the failed outcome and
-longitudinal metrics, ideally tested on the original event-level data rather
-than engineered to pass the present synthetic task.
+Finally, the synthetic BCI evidence has a hard ecological boundary. Outcome
+reward is supplied to the critic, the psychometric targets are calibrated to
+each trained policy on a separate split, and the task has experimenter-defined
+causal roles. The acute lesions show how the implemented signal is composed;
+they do not show that cortex uses the same decomposition. A stronger biological
+paper needs a prospective prediction tested on the original event-level data,
+and the still-failed longitudinal prediction should not be silently discarded.
 
 ## 9. Reproducibility statement
 
@@ -486,4 +585,5 @@ bash experiments/finalize_accept.sh
 
 It rechecks the main figures, theoretical identities, local-rule mechanics,
 baseline protocols, native-author records, standard-ResNet confirmation,
-failed biological confirmation, and the sealed standard-depth boundary.
+failed biological protocols, the passed calibrated BCI confirmation, and the
+sealed old standard-depth boundary.