path: root/BIAS_STORY_REASSESSMENT.md

# Structured-bias paper: ICLR reassessment

Date: 2026-08-06. This assessment deliberately gives no novelty credit to the
clean KP scaling results. It asks whether the Harnett-specific operation alone
can carry an ICLR paper:

```text
r = a - P(h)
Delta W = eta * r * eligibility
```

## Verdict

The topic is strong enough for ICLR, but the current evidence is not. With the
clean scaling story removed, the present package is approximately **5/10
(borderline reject)**. A coherent structured-bias paper can reach **6/10** if
it establishes a natural source of bias, predicts its learning dynamics, and
shows that the unchanged two-line SDIL rule removes it more effectively than
bias-specific alternatives at matched observation cost. It can reach a solid
**7/10** with a measured physical bias source or a second substantially
different system and a useful-scale confirmation.

This should not be sold as another general alternative to backpropagation.
The paper-level question is:

> When a local teaching channel mixes task instruction with a systematic,
> state-predictable component, what failure does that component cause, and can
> a locally learned neutral prediction remove it without removing instruction?

## What the repository currently proves

Positive evidence:

- In the controlled MNIST intervention, raw and norm-matched raw signals fall
  to about 10.3% at the strongest soma-predictable traffic, while innovation
  retains 97.35% over five seeds. Norm matching rules out a pure scale effect.
- In five paired CIFAR-10 ResNet-20 seeds, dynamic neutral projection remains
  within 0.2 points of clean KP under a four-times-RMS traffic intervention.
  The same mechanism remains finite through ResNet-56.
- The implementation records instruction-off observations, checks that no task
  loss or clean teaching signal enters the neutral fit, and retains failed
  gates and untouched endpoints.

Evidence that limits the claim:

- The strongest controlled bias is generated from the same somatic statistic
  made available to the predictor. Recovery is therefore a clean necessity
  test, but not evidence that important real bias normally has this form.
- The five-seed endogenous MNIST gate failed one of four traffic realizations.
  The FashionMNIST recovery found large gains but failed the no-traffic-gap
  requirement for top-down traffic. The necessary shared-context task produced
  only a 0.195-point innovation gain. These failures show that high predictor
  R-squared is not sufficient to identify instruction.
- A fixed predictor did not stabilize the standard ResNet intervention. The
  successful variant needs an instruction-off observation on every example and
  a fast affine projection. This is useful evidence, but it weakens the claim
  that the original two-line rule alone solves the problem.
- The clean ResNet-20/32/56 scaling belongs to reciprocal Kolen--Pollack credit.
  It establishes that the substrate scales, not that residualization causes
  scaling.
- The current theory proves a standard conditional-expectation projection
  identity and a generic smooth-loss descent condition. It does not yet predict
  a bias-induced error floor, limit cycle, instability, or depth law.

## Prior-work pressure

The novelty boundary is narrow:

- Laborieux et al. show that finite one-sided nudging gives EP a first-order
  estimator bias. Randomly reversing beta cancels that term in expectation at
  two-phase cost, although their CIFAR-10 runs have high variance; centered EP
  is stable but adds a phase.
- Holomorphic EP obtains exact finite-amplitude gradients through an oscillating
  complex nudge and reports ImageNet32 performance comparable to BP.
- Jacobian homeostasis separately analyzes finite-nudge and weight-asymmetry
  bias and repairs the latter on ImageNet32. This is direct precedent that a
  bias-mechanism paper can be accepted, but it sets a high bar for theory and
  scale.
- Dillavou et al. measure deterministic update bias in a physical contrastive
  resistor network, connect it to error floors, null-space drift and limit
  cycles, and introduce overclamping. Any physical contrastive experiment must
  compare to overclamping rather than presenting bias robustness as unclaimed
  territory.
- Conditional-mean subtraction itself is regression/control-variate machinery.
  The contribution cannot be the algebraic subtraction alone; it must be the
  locally identifiable bias model, its learning consequence, and causal
  evidence that the removed component is nuisance rather than instruction.

Primary sources:

- Harnett/Francioni et al., Nature 2026:
  https://www.nature.com/articles/s41586-026-10190-7
- Finite-nudge EP bias, Frontiers 2021:
  https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.633674/full
- Holomorphic EP, NeurIPS 2022:
  https://proceedings.neurips.cc/paper_files/paper/2022/hash/545a114e655f9d25ba0d56ea9a01fc6e-Abstract-Conference.html
- Jacobian homeostasis, ICLR 2024:
  https://proceedings.iclr.cc/paper_files/paper/2024/hash/6a55f024db3f771194bdadc8f3a35381-Abstract-Conference.html
- Physical imperfection and overclamping, arXiv v2 2026:
  https://arxiv.org/html/2505.22887

## The minimum complete mechanism

Use one explicit model throughout:

```text
a_t = s_t + b(z_t) + epsilon_t
r_t = a_t - P(z_t)
```

Here `s_t` is task instruction, `b(z_t)` is deterministic or slowly drifting
bias predictable from locally available neutral state `z_t`, and `epsilon_t`
is unpredictable zero-mean noise. The identification assumptions must be
stated, tested and violated deliberately:

1. the neutral observation contains no instruction;
2. the neutral and task periods share the same bias law;
3. the predictor uses information available before instruction;
4. the predictor class can represent enough of the bias;
5. predictor learning is slow or cross-fitted enough not to absorb instruction.

The theory must go beyond `P` minimizing mean-squared prediction error. At
minimum it should derive, for a tractable linear or locally linear system:

- the raw learner's displaced fixed point, error floor or limit cycle as a
  function of bias, curvature and learning rate;
- how coherent bias accumulates with steps, depth or flat directions, compared
  with zero-mean noise of the same RMS;
- an optimization/stability bound in terms of residual predictor error and
  neutral-to-task distribution shift;
- a failure theorem or counterexample showing when subtraction removes part of
  the instruction.

These statements generate quantitative curves before the large experiment.
Fitting a line after observing the endpoint is not sufficient.

## Best experimental systems

### 1. Physical coupled-learning imperfection -- primary candidate

The closest natural match is the resistor-network setting of Dillavou et al.
Its per-edge measurement/application bias persists when the correct
contrastive update shrinks, producing drift along flat directions and
multi-task limit cycles. An instruction-off or zero-error observation gives a
natural local baseline. The unchanged SDIL predictor should be tested against:
raw coupled learning, overclamping, a per-edge constant-offset calibration,
random zero-mean noise matched in RMS, and an oracle bias subtraction.

The decisive result is not only lower final error. SDIL must correctly predict
and reduce cycle span, null-space drift and forgetting using the bias measured
before task training. Real experimental traces or a bias distribution measured
from hardware would make this much stronger than a synthetic injection.

### 2. Deep contrastive network with hardware-shaped differential offsets

Use an author DCHN or Dual Propagation implementation and introduce offsets at
the actual local free/nudged measurement boundary, with distributions fixed
from a physical system rather than chosen as `P(h)` by construction. The task
ladder should include FashionMNIST, SVHN and CIFAR-10, with depth or relaxation
length varied. Compare the same two-line SDIL rule to raw learning, ordinary
constant calibration, overclamping where applicable, and clean/oracle paths.

This supplies useful-scale evidence, but by itself it remains simulation. A
five-seed artificial differential-bias result in Dual Propagation is supporting
evidence, not the central natural-bias claim.

### 3. Finite-nudge EP bias -- diagnostic, not primary

Random-sign beta is the correct same-cost baseline because it cancels the
first-order term in expectation. Centered EP is the lower-variance expensive
reference. Unless the original SDIL rule beats random-sign EP at matched phase
and observation cost without calibration stages or extra mechanisms, this
setting should be reported as a boundary where randomization already solves
the mean bias. It should not drive method design.

Weight-asymmetry EP is also unattractive as the first target because Jacobian
homeostasis is a specialized, accepted and large-scale solution.

## Causal experiment matrix

One compact matrix can distinguish the claimed mechanism:

| factor | required values | purpose |
|---|---|---|
| corruption | none, zero-mean noise, fixed bias, state-dependent bias, slow drift | separates noise robustness from bias removal |
| predictability | same RMS with low to high neutral R-squared | tests the proposed sufficient variable |
| signal rule | raw, norm-matched raw, static calibration, SDIL, oracle | isolates subtraction from scale and ordinary calibration |
| neutral validity | matched, shifted, instruction leakage | tests identification and failure boundaries |
| scale | task/depth/relaxation or number of alternating tasks | tests coherent accumulation |

The primary plots should be predetermined theory-to-data plots: error floor or
cycle span versus bias; degradation versus scale at fixed bias; recovery versus
neutral predictability; and accuracy/error versus measured observations or
wall time. Endpoint accuracy alone is not mechanism evidence.

## Score gates

**Current: 5/10.** Strong auditing and a large controlled effect, but the
natural importance, unique mechanism and theory are incomplete.

**Minimal accept, 6/10:**

- one natural or independently measured bias source;
- a theorem predicting the observed failure and residual dependence;
- unchanged two-line SDIL, without a new calibration stage;
- matched static-calibration and bias-specific baselines;
- three or more seeds on one useful-scale task plus causal negative controls;
- explicit neutral-shift and instruction-leakage failures.

**Solid accept, 7/10:** add either real physical-network evidence or a second
distinct learning system, and show the same frozen mechanism predicts both.

**Stop the ICLR push** if any of the following occurs:

- gains appear only when bias is constructed directly from the predictor's
  input;
- a constant offset/EMA calibration, overclamping, or random-sign beta matches
  SDIL at lower cost;
- useful-scale recovery requires per-example oracle-like neutral projection or
  another compound algorithm rather than the stated two-line rule;
- measured bias does not worsen with any meaningful scale variable;
- neutral observations absorb instruction under realistic distribution shift.

## Immediate decision

Continue only as a short, gated reassessment. First reproduce the physical
bias dynamics in the coupled-learning system and test the unmodified SDIL
predictor against overclamping and constant calibration. In parallel, finish
the already-frozen Dual Propagation differential-bias confirmation as
supporting evidence. Do not spend full CIFAR-scale compute until the simple
rule wins this mechanism gate. A failed gate should close the ICLR structured-
bias paper rather than trigger another compound recovery method.