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+# ICLR 2027 replan: local learning under structured bias
+
+## Decision
+
+Keep the title **Learning from the Unexpected: Somato-Dendritic Innovations
+for Local Credit Assignment**, but replace the old clean-scaling claim with a
+more defensible claim:
+
+> Two-state local learners can average away zero-mean noise, but not a
+> differential bias in their local teaching measurements. This bias produces
+> error floors, drift and task-switching cycles that can worsen as more local
+> populations are biased. A somato-dendritic innovation removes the locally
+> predictable component without backpropagation.
+
+The method remains frozen:
+
+```text
+r = a - P(z)
+Delta w = eta * r * eligibility
+```
+
+`P` is an affine or fixed-basis local adaptive filter trained by LMS on an
+instruction-off observation. No backbone-specific recovery stage, copied clean
+update, centered target, global loss or backpropagated signal may train it.
+
+The debiasing mechanism is already de-risked in synthetic experiments. The
+paper is not yet de-risked: the main remaining question is whether the same
+locally identifiable operation beats strong bias-specific corrections on a
+measured physical model and transfers unchanged across local-learning
+backbones.
+
+## What the paper must and must not claim
+
+The paper may claim that structured differential bias does not disappear with
+averaging and that its aggregate estimator norm can grow with the number of
+biased local blocks. It may claim task-level scaling only under explicit
+curvature, bias-alignment and identifiability assumptions, followed by the
+corresponding experiment. It must not claim that EP, Dual Propagation or local
+learning in general cannot scale cleanly.
+
+The comparison target is each backbone's strongest correction for the stated
+bias, not BP accuracy in a clean setting. Clean BP and clean native training
+are reference points. A result counts as SDIL only after it passes
+`HARDWARE_LOCALITY_CONTRACT.md`.
+
+## Critical path and hard gates
+
+### Gate 0: freeze the measurement and adapter contract
+
+Deliverables:
+
+- one adapter API exposing local neutral state, local teaching measurement and
+  native local eligibility;
+- one shared affine/fixed-basis predictor and neutral schedule across comparable
+  digital backbones;
+- no-grad, local-replay, downstream-independence, instruction-isolation,
+  common-mode and phase/storage tests;
+- a cost ledger counting phases/observations, stored local scalars, local
+  multiplies, wall time and peak memory;
+- exact definitions of common-mode bias, differential fixed bias,
+  state-dependent bias, slow drift and same-RMS zero-mean noise.
+
+Do not start expensive scale runs until this gate passes. This prevents a
+software-only implementation or a backbone-specific correction from becoming
+the reported method.
+
+### Gate 1: P1 measured-physical mechanism
+
+Recreate the released small-network coupled-learning dynamics using the
+published equations and measured bias traces. Freeze the released task pairs,
+bias traces, neutral cadence and predictor basis before comparing:
+
+1. standard coupled learning;
+2. same-RMS zero-mean noise;
+3. constant per-edge calibration;
+4. overclamping;
+5. unchanged SDIL;
+6. oracle subtraction.
+
+Sweep cycle period and bias/drift strength over every compatible released
+small-network instance. Report error floor, null-space drift, cycle span,
+forgetting and charged cost. Use simulator initializations for uncertainty;
+never present them as new independent hardware samples.
+
+Pass conditions:
+
+- structured bias remains after temporal averaging while matched zero-mean
+  noise decreases;
+- SDIL closes a substantial fraction of the raw-to-oracle gap on every
+  identifiable state-dependent-bias setting;
+- SDIL beats constant calibration when the bias varies with local state;
+- SDIL is nondominated with overclamping on error/cycle span versus charged
+  observation and circuit cost;
+- common-mode bias gives no artificial SDIL advantage;
+- task leakage and neutral-to-task shift fail in the direction predicted by
+  the identifiability theory.
+
+If SDIL loses to constant calibration or overclamping without a cost or
+generality advantage, stop the physical-first paper and reassess before using
+more GPU time.
+
+### Gate 2: frozen-adapter transfer
+
+Use the identical predictor family, update rule and neutral schedule in all
+paper-facing digital experiments. Only the native local teaching measurement
+and eligibility change with the backbone.
+
+**Dual Propagation.** Complete the frozen five-seed miniCNN confirmation, then
+run MiniCNN, VGG-style middle scale and author VGG16. The main conditions are
+native clean, raw differential bias, constant calibration, SDIL and oracle;
+same-RMS noise is the causal control. Use one seed to screen mechanics, three
+seeds for the accept matrix and five only for final claims. One existing author
+VGG16 run takes about 23,120 seconds (6.4 hours) on a GTX 1080, so VGG16 cells
+should move to the A6000 after the one-seed gate.
+
+**Equilibrium propagation.** Use the author DCHN implementation unchanged for
+native dynamics and hyperparameters, with a hand-written paper-facing local
+adapter. First test measurement/readout bias, for which constant calibration
+and oracle subtraction are matched baselines. Separately test finite-nudge
+bias against random-sign beta and centered EP. Do not claim SDIL solves
+finite-nudge bias unless the neutral observation actually identifies it.
+
+**Coupled-learning DCHN.** After EP passes, transfer the same adapter within the
+same codebase. Compare with constant calibration, centered coupled learning and
+an overclamping analogue. This is the cleanest digital bridge to the measured
+physical experiment.
+
+The accept matrix stops at three seeds and three families: measured physical
+coupled learning, Dual Propagation, and EP. Digital coupled learning is added
+as soon as the same adapter transfers; it is not allowed to delay the first
+complete paper draft.
+
+Pass conditions:
+
+- the raw biased endpoint worsens with the declared scale axis in at least two
+  independent families;
+- neutral predictability, rather than corruption RMS, predicts recovery;
+- SDIL improves every biased raw endpoint and beats constant calibration on
+  state-dependent bias;
+- SDIL is competitive with each family's strongest correction after cost is
+  charged;
+- the frozen adapter passes the executable BP-free audit in every family;
+- clean/common-mode controls show that the gain is not ordinary regularization
+  or a changed optimizer.
+
+Failure on one family narrows the scope. It must not trigger a new
+backbone-specific SDIL variant.
+
+## Theory package
+
+The theory and experiments share one bias model,
+
+```text
+a_l = s_l + b_l(z_l) + epsilon_l.
+```
+
+The accept version needs five results:
+
+1. a bias--variance decomposition showing that averaging removes the
+   conditionally zero-mean term but leaves the predictable differential term;
+2. an aggregate-block result, carefully stated as estimator scaling rather
+   than a universal loss theorem;
+3. local quadratic dynamics predicting displaced optima, null-space drift and
+   alternating-task cycle span;
+4. a residual bound decomposing predictor approximation, sample error, drift
+   and neutral-to-task shift;
+5. an instruction-preservation condition plus explicit leakage and
+   distribution-shift counterexamples.
+
+Each theoretical quantity must appear on an experimental axis. A theorem that
+does not predict a plotted transition, slope or failure control stays in the
+appendix or is removed.
+
+## Main figures
+
+1. **Problem and real evidence:** Harnett residual motivation, the two-state
+   measurement model, and the released physical error plateau/cycle drift.
+2. **Mechanism on the physical model:** matched noise versus bias; raw,
+   calibration, overclamping, SDIL and oracle; error/cycle span against charged
+   cost.
+3. **Scaling across backbones:** performance gap and residual bias versus depth,
+   width, biased-block count and task difficulty for physical learning, DP and
+   EP/CpL.
+4. **What makes recovery possible:** neutral predictability, drift rate,
+   task leakage and distribution shift, followed by an accuracy--cost Pareto
+   summary.
+
+The key scaling plot must show both the failure term and its removal. Plotting
+only final accuracy across larger clean architectures is not evidence for the
+new claim.
+
+## Three publication bars
+
+### Accept bar: target reviewer score 6--7
+
+- Gates 0--2 pass for the measured physical model, DP and EP;
+- all main comparisons have three seeds or every released physical replicate;
+- the five theory results predict the main experiments;
+- the method remains two lines and executable without BP;
+- strong baselines, cost accounting, negative controls and failure cases are
+  present;
+- the complete manuscript makes the conditional claim, not a universal
+  anti-EP claim.
+
+At this point the paper has a new problem, a biologically motivated local
+operation, real measured evidence, causal controls and cross-backbone transfer.
+
+### Oral-B bar: target reviewer score 7--8
+
+- extend EP and coupled-learning DCHNs across FashionMNIST, SVHN and CIFAR-10,
+  multiple depths/biased-block counts and five seeds;
+- finish five-seed VGG16 DP and the full cost Pareto comparison;
+- cross fixed, state-dependent and slowly drifting bias with matched noise,
+  predictability and neutral/task shift;
+- keep one frozen predictor configuration across comparable backbones;
+- show that measured or independently fitted bias parameters predict the
+  observed scaling curves without post-hoc tuning.
+
+This bar is breadth plus prediction: the same mechanism must explain when SDIL
+works, how much it recovers and when it fails.
+
+### Oral-A bar: target reviewer score 8--9
+
+- demonstrate the local filter and subtraction path in circuit simulation or
+  on physical hardware, or validate on a second independently measured
+  substrate;
+- complete long large-scale runs such as CIFAR-100/large DCHNs only after the
+  adapter is frozen;
+- compare end-to-end phase, storage, precision, time and energy costs rather
+  than only optimizer steps;
+- obtain a prospective prediction: select bias/predictability conditions from
+  small systems, then correctly predict recovery and failure on the held-out
+  larger system.
+
+These long runs may use spare GPUs in parallel after Gate 0, but their design
+must not change in response to their final endpoints.
+
+## Execution order and rough budget
+
+1. **Now (hours):** let the existing DP C1 run finish; audit all five seeds and
+   commit only the completed frozen result.
+2. **Next 1--3 working days:** implement and test the released small physical
+   model, finish Gate 0, and run the cheap P1 CPU sweep.
+3. **Following week:** write theory v0 beside the P1 plots; run one-seed DP
+   scale and EP screens. Reject broken adapters before replication.
+4. **Following 1--2 weeks:** run the three-seed accept matrix on the 1080s and
+   OscarWX's A6000; build the four main figures and write a full paper draft.
+5. **After a reviewer-style audit reaches 6:** launch five-seed/multi-dataset
+   Oral-B jobs. Start Oral-A long runs opportunistically on otherwise idle
+   cards.
+
+Expected GPU exposure is roughly 40--100 hours for screened accept-scale
+digital runs and several hundred GPU hours for Oral-B breadth. These are
+planning ranges, not promises: actual per-cell time must be recorded from the
+one-seed screens before the full launch. P1 and theory are the immediate
+critical path; adding GPUs cannot compensate for failure there.
+
+## Reviewer-score ledger
+
+Update one evidence table after every completed gate:
+
+| item | evidence | strongest alternative | unresolved failure | score effect |
+|---|---|---|---|---|
+| real bias exists | released physical P0 | overclamping paper | no SDIL fix yet | necessary, not sufficient |
+| physical mechanism | P1 | calibration/overclamping | pending | unlocks 6 |
+| general transfer | DP + EP/CpL | native correction | pending | supports 6--7 |
+| scaling prediction | theory + held-out scales | descriptive curve | pending | supports 7--8 |
+| hardware realization | circuit/second substrate | primitive argument | pending | supports 8--9 |
+
+Every progress report should state positive results, negative results, compute
+and a fresh reviewer score. Accuracy without the matched strongest baseline,
+cost coordinate or frozen protocol does not move the score.