paper: add evidence-bound ICLR manuscript draft

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diff --git a/paper/CLAIM_LEDGER.json b/paper/CLAIM_LEDGER.json
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+{
+  "figures": [
+    "../results/figs/figure1_pareto.png",
+    "../results/figs/figure2_scaling.png",
+    "../results/figs/figure3_innovation.png",
+    "../results/figs/figure4_resnet_confirmation.png"
+  ],
+  "gate_statuses": [
+    {
+      "expected": "passed",
+      "source": "results/kp_dynamic_projection_confirmation_gate.json"
+    },
+    {
+      "expected": "failed",
+      "source": "results/bci_td_confirmation_gate.json"
+    }
+  ],
+  "manuscript": "paper/MANUSCRIPT.md",
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+      "source": "results/figs/figure4_resnet_confirmation_manifest.json",
+      "token": "2.02"
+    },
+    {
+      "format": "fixed2",
+      "id": "d4_wall_ratio",
+      "pointer": "/statistics/dynamic_wall_ratio_to_clean_kp_mean",
+      "source": "results/figs/figure4_resnet_confirmation_manifest.json",
+      "token": "1.47"
+    },
+    {
+      "format": "percent3_percent",
+      "id": "r2_final_performance",
+      "pointer": "/metrics/intact_final/mean",
+      "source": "results/bci_td_confirmation_gate.json",
+      "token": "99.531%"
+    },
+    {
+      "format": "percent3",
+      "id": "r2_learning_gain",
+      "pointer": "/metrics/intact_gain/mean",
+      "source": "results/bci_td_confirmation_gate.json",
+      "token": "90.451"
+    },
+    {
+      "format": "percent3",
+      "id": "r2_plasticity_margin",
+      "pointer": "/metrics/plasticity_half_margin/mean",
+      "source": "results/bci_td_confirmation_gate.json",
+      "token": "45.191"
+    },
+    {
+      "format": "fixed4",
+      "id": "r2_role_cosine",
+      "pointer": "/metrics/role_cosine/mean",
+      "source": "results/bci_td_confirmation_gate.json",
+      "token": "0.9395"
+    },
+    {
+      "format": "percent3_percent",
+      "id": "r2_residual_outcome_accuracy",
+      "pointer": "/metrics/residual_outcome_accuracy/mean",
+      "source": "results/bci_td_confirmation_gate.json",
+      "token": "47.325%"
+    },
+    {
+      "format": "abs_percent3",
+      "id": "r2_residual_soma_gap",
+      "pointer": "/metrics/residual_soma_outcome_gap/mean",
+      "source": "results/bci_td_confirmation_gate.json",
+      "token": "4.322"
+    },
+    {
+      "format": "fixed3",
+      "id": "r2_longitudinal_prediction",
+      "pointer": "/metrics/longitudinal_prediction/mean",
+      "source": "results/bci_td_confirmation_gate.json",
+      "token": "-0.013"
+    }
+  ],
+  "required_boundary_text": [
+    "Neither mechanism is claimed as new.",
+    "we do not infer that cortex implements BP;",
+    "we do not claim arbitrary top-down traffic removal;",
+    "It does not support a ResNet-20-to-56 depth claim.",
+    "The joint biological gate nevertheless fails.",
+    "we do not call this variant single-phase."
+  ],
+  "required_reference_urls": [
+    "https://doi.org/10.1038/s41586-026-10190-7",
+    "https://openreview.net/forum?id=ByeUBANtvB",
+    "https://papers.nips.cc/paper_files/paper/2019/hash/f387624df552cea2f369918c5e1e12bc-Abstract.html",
+    "https://doi.org/10.64898/2026.06.16.732595"
+  ],
+  "version": 1
+}
diff --git a/paper/MANUSCRIPT.md b/paper/MANUSCRIPT.md
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+# Learning from the Unexpected: Somato-Dendritic Innovations for Local Credit Assignment
+
+**Anonymous authors — working ICLR 2027 draft**
+
+> This is the evidence-bound manuscript source. It intentionally contains the
+> failed biological and scaling gates. Author names, acknowledgements, and the
+> venue template are left unset rather than inferred.
+
+## Abstract
+
+Local-learning models often identify activity in a feedback or apical
+compartment with a teaching signal. That compartment can also carry ordinary
+state and contextual traffic, so its raw activity need not point in a useful
+learning direction. Motivated by the per-neuron somato-dendritic residuals
+measured by Francioni and colleagues, we define the teaching variable as an
+*innovation*: apical activity minus its neutral-period prediction from the same
+neuron's somatic activity. We evaluate this operation on top of two inherited
+credit pathways—node-perturbation-trained direct feedback and reciprocal
+Kolen--Pollack plasticity—and attribute those pathways to prior work. Conditional
+expectation makes the innovation the minimum-power residual within the
+predictor's somatic information class, while a norm-matched raw control shows
+that subtraction changes direction rather than merely gain. In a frozen
+60-run intervention, raw and norm-matched apical signals fall to 10.38% and
+10.31% MNIST accuracy under strong soma-predictable traffic, whereas innovation
+retains 97.348%. Across a 12-fold increase in hidden depth on flattened
+CIFAR-10, the learned-feedback backbone changes by -0.214 points while direct
+feedback alignment loses early-layer alignment. On a standard ResNet-20, a
+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.
+
+## 1. Introduction
+
+Credit assignment asks how a synapse or neuron should change given only a
+distant behavioral outcome. Backpropagation solves this problem in artificial
+networks by transporting exact reverse-mode derivatives, but its weight
+transport, global computation graph, and update-locking requirements make it an
+imperfect model of biological learning. Fixed feedback alignment (FA) and
+direct feedback alignment (DFA) relax exact transport, while equilibrium
+propagation, Forward--Forward, PEPITA, Dual Propagation, and dendritic or
+burst-based models change the activity dynamics or phase structure of learning.
+These approaches differ substantially in what is local, what is transported,
+and what computational cost is hidden in a “phase.”
+
+Compartmental models commonly separate basal feedforward activity from apical
+feedback. This is a powerful architectural idea, but it leaves an identification
+problem: feedback compartments carry state, context, prediction, and ordinary
+coupling in addition to any instruction. Treating all apical activity as error
+can therefore rotate a local update even when the feedback pathway itself is
+well calibrated.
+
+[Francioni et al. (2026)](https://doi.org/10.1038/s41586-026-10190-7)
+provide a sharper empirical object. For each cortical neuron they model the
+normal relation between somatic and distal dendritic event magnitudes and study
+the residual. At population level those residuals carry information absent from
+soma alone, including outcome and neuron-specific causal-role signatures. The
+experiments establish a vectorized dendritic signal, but do not establish that
+the residual directly updates synapses or that cortex implements
+backpropagation.
+
+We turn the residual operation itself into a falsifiable learning hypothesis:
+use only the component of apical activity that is unexpected from the same
+cell's neutral somatic state. We call this **Somato-Dendritic Innovation
+Learning (SDIL)**. The credit pathway that supplies the instructional component
+is deliberately modular. In small feedforward networks we train direct feedback
+with causal node perturbations, following
+[Lansdell, Prakash, and Kording (2020)](https://openreview.net/forum?id=ByeUBANtvB).
+For the stable ResNet experiment we use reciprocal plasticity following
+[Akrout et al. (2019)](https://papers.nips.cc/paper_files/paper/2019/hash/f387624df552cea2f369918c5e1e12bc-Abstract.html).
+Neither mechanism is claimed as new.
+
+Our contributions are:
+
+1. a per-cell neutral innovation rule, with raw and per-example norm-matched
+   controls that isolate subtraction from gain;
+2. a conditional-projection analysis, a descent/gain boundary, and explicit
+   perturbation variance and resource accounting;
+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.
+
+## 2. Somato-dendritic innovation learning
+
+### 2.1 Local teaching variable
+
+For hidden layer \(l\),
+
+\[
+u_l = W_l h_{l-1}, \qquad h_l = \phi(u_l).
+\]
+
+The apical compartment mixes an instruction \(s_l=A_lc_l\) with ordinary
+traffic \(n_l\):
+
+\[
+a_l = s_l+n_l.
+\]
+
+A per-cell predictor is fitted when task instruction is absent:
+
+\[
+m_l(h_l) \approx \mathbb{E}[n_l\mid\mathcal{F}_{h_l},\mathrm{neutral}].
+\]
+
+The teaching variable is
+
+\[
+r_l=a_l-m_l(h_l).
+\]
+
+The implemented small-network predictor is diagonal affine,
+\(m_{l,i}(h_{l,i})=p_{l,i}h_{l,i}+b_{l,i}\). This restriction matters:
+traffic predictable only from other neurons or hidden contextual state cannot
+be guaranteed to disappear.
+
+With a static feedforward eligibility,
+
+\[
+\Delta W_l
+=
+\eta_W\left(r_l\odot\phi'(u_l)\right)h_{l-1}^{\mathsf T}.
+\]
+
+Every factor in this update is available at the synapse or its postsynaptic
+cell: presynaptic activity, local activation gain, and the same cell's apical
+innovation. Recurrent or spiking implementations can replace the instantaneous
+eligibility with a local temporal trace; that extension is not evaluated here.
+
+### 2.2 Two inherited instructional pathways
+
+In the learned-direct-feedback experiments, occasional antithetic Rademacher
+perturbations estimate a causal hidden-state direction. If
+\(\xi_l\) is injected at layer \(l\) and \(L_+,L_-\) are the paired losses,
+
+\[
+q_l
+=
+-\frac{L_+-L_-}{2\sigma}\xi_l.
+\]
+
+The feedback map predicts this direction:
+
+\[
+\Delta A_l
+=
+\eta_A(q_l-A_lc_l)c_l^{\mathsf T}.
+\]
+
+This is learned synthetic feedback by node perturbation and is inherited from
+Lansdell et al. It requires logical loss observations during calibration, which
+we report separately from ordinary supervised loss computation.
+
+The standard ResNet branch instead uses a modified Kolen--Pollack reciprocal
+path. Forward and feedback synapses independently recompute matched local
+activity products and use matched decay; neither path reads or copies the
+other's weight tensor. This inherited mechanism eliminates task-loss queries
+but adds a second reciprocal local correlation.
+
+### 2.3 Dynamic neutral projection
+
+A fixed neutral predictor was unstable in the ResNet mixed-traffic
+intervention. Linearization exposes a multiplicative residual operator whose
+positive modes can diverge even when the initial predictor sign is correct.
+The successful ResNet variant retains a slow neutral predictor and adds a
+paired instruction-off observation on every ordinary example. For each cell,
+
+\[
+e^0_l=a^0_l-m_l(h_l)
+\]
+
+is the remaining neutral residual, and the local affine projection coefficient
+is
+
+\[
+q_l
+=
+\frac{\mathrm{Cov}(e^0_l,h_l)}
+       {\mathrm{Var}(h_l)}.
+\]
+
+The stabilized instruction is the task-period apical signal after removing the
+neutral mean and current affine soma coupling. The fit sees no task instruction,
+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.
+
+## 3. What residualization guarantees—and what it does not
+
+Let \(n\) be neutral apical traffic and let
+\(\mathcal{F}_h\) be the somatic information available to the predictor. The
+conditional mean \(m(h)=\mathbb{E}[n\mid\mathcal{F}_h]\) obeys
+
+\[
+\mathbb{E}\langle n-m(h),g(h)\rangle=0
+\]
+
+for every square-integrable soma-measurable \(g\), and
+
+\[
+\mathbb{E}\|n-g(h)\|^2
+=
+\mathbb{E}\|n-m(h)\|^2+\mathbb{E}\|m(h)-g(h)\|^2.
+\]
+
+Thus the conditional mean is the unique minimum-power subtraction within the
+chosen somatic information class. This is a standard projection identity, not
+a new theorem. If the neutral traffic law is invariant and task apical
+activity is \(a=s+n\), the innovation is \(r=s+n-m(h)\). The result says
+nothing about nuisance independent of \(\mathcal{F}_h\), nor does it prove that
+\(r\) is a gradient.
+
+The norm-matched raw control uses
+\(\alpha a\), where \(\alpha=\|r\|/\|a\|>0\) for each example. Since
+
+\[
+\cos(\alpha a,-g)=\cos(a,-g),
+\]
+
+positive gain matching cannot reproduce a directional benefit at a fixed
+state. We still train the matched control end to end because gain changes later
+states and optimization trajectories.
+
+Finally, hidden-state alignment alone is insufficient. For complete parameter
+gradient \(p=\nabla_\theta L\) and proposed update direction \(v\), a
+\(\beta\)-smooth loss satisfies
+
+\[
+L(\theta+\eta v)-L(\theta)
+\le
+-\eta c\|p\|\|v\|
++\frac{\beta\eta^2}{2}\|v\|^2,
+\]
+
+where \(c=\langle-p,v\rangle/(\|p\|\|v\|)\). Descent requires direction, gain,
+and curvature to agree. This distinction explains why some failed branches
+end with nearly perfect feedback-weight cosine yet have already diverged.
+
+## 4. Experimental protocol
+
+We freeze advancement gates before opening the corresponding held-out
+endpoint. Failed branches, dirty-provenance rejection, seed sets, and
+validation/test boundaries remain in the repository. Exact hyperparameters,
+hashes, and hardware-independent cost definitions are in the accompanying
+manifests.
+
+### Controlled traffic and depth
+
+The primary necessity experiment trains depth-3, width-256 MNIST networks for
+15 epochs across predictable-traffic ratios
+\(\rho\in\{0,0.05,0.2,0.5\}\), three teaching signals, and seeds 0--4. The
+signals are raw apical activity, per-example norm-matched raw activity, and
+innovation.
+
+The depth panel trains width-64 residual MLPs on flattened CIFAR-10 at hidden
+depths 5, 10, 20, 30, and 60, with five paired seeds. BP is a nonlocal
+reference; FA, DFA, and learned node-perturbation feedback are local-learning
+comparators. Because accuracy does not improve with depth in this task, the
+panel tests preservation and credit alignment rather than useful
+representation depth.
+
+### Standard ResNet confirmation
+
+The standard-scale test uses a CIFAR ResNet-20 (\(6n+2\), width 16) trained for
+200 epochs on all 50,000 training examples. D3 selects no confirmation seed:
+it is a single validation-only endpoint. D4 then trains clean reciprocal credit
+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.
+
+### Baselines and cost
+
+In-repository controls include BP, FA, DFA, direct node perturbation,
+Forward--Forward, PEPITA, canonical persistent-particle equilibrium
+propagation, hierarchical FA, response-mirror variants, and reciprocal
+Kolen--Pollack learning. We additionally audit author-code BurstCCN and Dual
+Propagation runs under their native protocols. Native architectures,
+preprocessing, selection semantics, and wall times are reported as context,
+not placed on a purported equal-compute frontier.
+
+We report logical task-loss observations, affine MAC estimates, separately
+estimated elementwise work, forward-equivalent examples, paired neutral
+observations, peak allocated memory, and wall time. A zero task-loss-query count
+does not make neutral observations or local correlations free.
+
+## 5. Results
+
+### 5.1 Innovation is necessary under soma-predictable traffic
+
+![Somato-dendritic innovation mechanism and controlled necessity experiment.](../results/figs/figure3_innovation.png)
+
+**Figure 1: Controlled innovation necessity.** The committed figure renderer
+audits all 60 records and displays mean and individual-seed outcomes.
+
+At \(\rho=0\), all three signal rules coincide at 97.45%. At \(\rho=0.5\),
+raw apical and norm-matched raw learning fall to 10.38% and 10.31%, whereas
+innovation reaches 97.348%. The paired gain over norm-matched raw is
+87.038 ± 0.607 points. Used-signal alignment remains positive for innovation
+and becomes negative for both controls. The matched control rules out a
+per-example magnitude-only explanation.
+
+This intervention is deliberately strong and structured: traffic is generated
+from the predictor's available somatic statistic. A separate broad endogenous
+top-down intervention does not show uniform removal. The supported claim is
+therefore conditional nuisance removal, not arbitrary contextual denoising.
+
+### 5.2 Learned local credit preserves accuracy over depth in a controlled task
+
+![Accuracy, BP gap, and early credit alignment across hidden depth.](../results/figs/figure2_scaling.png)
+
+**Figure 2: Controlled depth preservation.** “Scaling” in this panel denotes
+retention under a 12-fold increase in hidden depth, not a benefit from added
+depth.
+
+The learned-feedback backbone changes by -0.214 ± 0.349 accuracy points from
+depth 5 to 60. Its paired one-sided test rejects a drop of at least one point
+(\(p=0.0037\)). DFA changes by -0.870 ± 0.358 points and does not reject the
+same margin (\(p=0.2310\)); its early-layer alignment falls from 0.514 to
+0.047. SDIL-labelled no-traffic runs in this panel reduce to the inherited
+Lansdell-style feedback learner and do not establish the novelty of
+residualization.
+
+![Local-learning Pareto frontier and exact-architecture EP comparison.](../results/figs/figure1_pareto.png)
+
+**Figure 3: Accuracy and measured wall-time context.** The local frontier is
+constructed only within the matched flattened-CIFAR protocol. The EP panel
+matches forward architecture, examples, and epochs while retaining each
+method's native preprocessing and dynamics.
+
+Canonical EP is the strongest conceptual stability baseline in the repository
+but is expensive in the audited implementation: at two hidden layers its mean
+wall time is 17,530 s and mean test accuracy is 89.526%, versus 58.5 s and
+97.516% for the feedforward local learner under the stated method-native
+dynamics. These numbers are not a hardware-independent dominance theorem; they
+make the measured cost mismatch explicit.
+
+### 5.3 Dynamic innovation survives untouched ResNet-20 confirmation
+
+![Paired ResNet-20 endpoint, layerwise signal alignment, and trajectory audit.](../results/figs/figure4_resnet_confirmation.png)
+
+**Figure 4: Standard-ResNet confirmation.** All panels are regenerated from
+the ten untouched D4 records, and the renderer refuses missing seeds,
+protocol drift, dirty provenance, or disagreement with the frozen gate.
+
+Dynamic innovation reaches 91.584% mean test accuracy versus 91.388% for clean
+reciprocal credit. Its mean paired advantage is 0.196 points; the one-sided 95%
+upper confidence bound on the clean-minus-dynamic deficit is 0.131 points,
+well inside the frozen 2.5-point noninferiority margin. All five dynamic seeds
+reach at least 91.51%, and mean early-third teaching alignment is 0.999687.
+
+The raw apical direction is poorly aligned in early layers under four-times-RMS
+traffic, whereas the projected innovation remains nearly collinear with the
+negative hidden-state gradient. Reciprocal feedback tracks the forward path
+throughout training rather than merely reaching an aligned endpoint.
+
+The dynamic run uses 1.326 times the matched BP affine-MAC estimate, zero
+logical task-loss queries, one paired neutral observation per ordinary example,
+and 2.02 GiB mean peak allocated memory. On the same GTX 1080s its mean wall
+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
+
+The original online-control/desired-velocity screen fails its frozen causal
+sign and acute-control gates. We therefore constructed a separate
+temporal-difference recovery that factorizes a slowly learned causal-role
+vectorizer from within-episode performance dynamics. Its development screen
+selects one learning rate before confirmation.
+
+The untouched confirmation contains six task seeds and five model seeds per
+task. It learns successfully: mean final performance is 99.531%, mean learning
+gain is 90.451 points, the plasticity lesion has a 45.191-point half-margin,
+role cosine is 0.9395, and all 30 records have the predicted positive sign
+inversion. Residuals are also strongly decorrelated from soma, and their
+correlation with error velocity exceeds that with error magnitude.
+
+The joint biological gate nevertheless fails. Residual outcome decoding is
+47.325%, 4.322 points below soma rather than above it, and early residuals do
+not predict late somatic change (mean correlation -0.013). Surrounding-network
+event accuracy also narrowly misses its frozen mean threshold. Thus the model
+learns a causal-role temporal-difference plasticity signal without reproducing
+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.
+
+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.
+
+## 7. Related work
+
+FA replaces exact transpose transport with random feedback
+([Lillicrap et al., 2016](https://doi.org/10.1038/ncomms13276)); DFA sends fixed
+random output feedback directly to each hidden layer
+([Nøkland, 2016](https://arxiv.org/abs/1609.01596)). Learned synthetic feedback
+by causal perturbation is closest to SDIL's small-network instructional
+backbone ([Lansdell et al., 2020](https://openreview.net/forum?id=ByeUBANtvB)).
+Akrout et al.'s weight-mirror and modified Kolen--Pollack mechanisms are the
+source of the stable reciprocal ResNet substrate.
+
+Compartmental and burst-based approaches already establish that segregated
+dendrites can coordinate local credit. These include segregated-dendrite
+learning ([Guerguiev et al., 2017](https://doi.org/10.7554/eLife.22901)),
+dendritic microcircuits
+([Sacramento et al., 2018](https://papers.nips.cc/paper_files/paper/2018/hash/1dc3a89d0d440ba31729b0ba74b93a33-Abstract.html)),
+and burst-dependent plasticity
+([Payeur et al., 2021](https://doi.org/10.1038/s41593-021-00857-x)).
+The recent BurstCCN preprint explicitly connects hierarchical dendritic credit
+to the Francioni/Harnett observations
+([Greedy et al., 2026](https://doi.org/10.64898/2026.06.16.732595)).
+Consequently, “apical dendrites carry vector errors” and “a dendritic method
+trains a convolutional network” are not our novelty claims.
+
+Equilibrium propagation uses free and nudged energy-based relaxation
+([Scellier and Bengio, 2017](https://doi.org/10.3389/fncom.2017.00024)).
+Dual Propagation represents activity and error in dyadic states within one
+inference phase
+([Høier and Zach, 2023](https://proceedings.mlr.press/v202/hoier23a.html);
+[Høier and Zach, 2024](https://openreview.net/forum?id=ui8ewXg1hV)).
+PEPITA modulates the input with output error and uses a second forward
+computation
+([Dellaferrera and Kreiman, 2022](https://proceedings.mlr.press/v162/dellaferrera22a.html)).
+Forward--Forward trains layer-local goodness on positive and negative data
+([Hinton, 2022](https://www.cs.toronto.edu/~hinton/FFA13.pdf)).
+SDIL differs in the variable it extracts from a mixed feedback compartment:
+the instruction is the component unexpected from neutral soma, not raw
+feedback, a two-state contrast, or an input perturbation.
+
+## 8. Limitations and discussion
+
+The central novelty is narrow. Conditional projection is standard, and both
+instructional pathways are inherited. The empirical contribution is that
+subtraction is load-bearing under a controlled mixed-channel intervention and
+that the dynamic version remains stable at ResNet-20 scale.
+
+The predictor's information class is a hard boundary. A diagonal per-cell
+predictor cannot remove population-only or latent traffic. Neutral periods are
+also an assumption: task-period fitting can absorb the soma-predictable part of
+the teaching signal itself, while distribution shift can make a correct neutral
+predictor wrong during behavior.
+
+The paired-neutral controller improves stability but weakens a single-phase
+biological interpretation and adds substantial wall time despite modest affine
+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.
+
+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.
+
+## 9. Reproducibility statement
+
+Every publication-facing result is bound to source revision, exact seed set,
+protocol, split, and evaluation boundary. Renderers regenerate figures from
+raw records and write source-hash manifests. The complete mechanical audit is:
+
+```bash
+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.
diff --git a/paper/manuscript_audit.json b/paper/manuscript_audit.json
new file mode 100644
index 0000000..35cfcaa
--- /dev/null
+++ b/paper/manuscript_audit.json
@@ -0,0 +1,275 @@
+{
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+    },
+    {
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+}