ewe-gate 0.1.0

Epistemic Weight Engine — Pre-update gating for noise-robust AI learning

ewe-gate

Epistemic Weight Engine — A plug-and-play pre-update gating mechanism for noise-robust AI learning.

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What is EWE?

Current AI systems treat every training sample equally — regardless of whether the information is reliable, novel, or approval-biased.

EWE adds a gate before every parameter update that asks three questions:

Layer	Signal	Question
I(x)	Gradient magnitude	Does this sample actually matter?
R(x)	Evidence vs approval	Is this label reliable or just popular?
P(x)	Loss deviation	Is this genuinely new information?

Only samples that pass the gate trigger a parameter update. Everything else is skipped.

Result on CIFAR-10N with 40% real human label noise:

Method	Accuracy
Standard Training	72.37%
EWE (ours)	79.36% (+6.99%)
EWE + GCE (ours)	84.33% (+11.96%)

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Install

pip install ewe-gate

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Quick Start

Option 1 — Just the gate (most flexible)

import torch
import torch.nn as nn
from ewe import EWEGate

model = YourModel()
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
criterion = nn.CrossEntropyLoss(reduction='none')
gate = EWEGate()

for epoch in range(50):
    for x, y in dataloader:
        optimizer.zero_grad()
        outputs = model(x)
        losses = criterion(outputs, y)

        # EWE filters which samples update parameters
        filtered_loss = gate.filter_losses(losses, outputs.detach())
        if filtered_loss is not None:
            filtered_loss.backward()
            optimizer.step()

    print(f"Epoch {epoch} | Accept rate: {gate.acceptance_rate:.1%}")

Option 2 — EWETrainer (simplest)

from ewe import EWETrainer
import torch.nn as nn

trainer = EWETrainer(
    model=model,
    optimizer=optimizer,
    criterion=nn.CrossEntropyLoss(reduction='none'),
)

for epoch in range(50):
    loss, acc, rate = trainer.train_epoch(train_loader)
    val_acc = trainer.evaluate(val_loader)
    print(f"Epoch {epoch} | Loss: {loss:.3f} | Acc: {acc:.1f}% | Accept: {rate:.1%}")

Option 3 — EWE + GCE combined (best results)

from ewe import EWEGate
from ewe import GCELoss

gate = EWEGate()
criterion = GCELoss(q=0.7)  # Noise-robust loss

for x, y in dataloader:
    optimizer.zero_grad()
    outputs = model(x)
    losses = criterion(outputs, y, reduction='none')

    filtered_loss = gate.filter_losses(losses, outputs.detach())
    if filtered_loss is not None:
        filtered_loss.backward()
        optimizer.step()

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Configuration

gate = EWEGate(
    alpha=0.45,      # Weight for Impact module
    beta=0.40,       # Weight for Reality Alignment (most important)
    gamma=0.15,      # Weight for Paradigm Shift
    k=0.25,          # Gate sensitivity (higher = stricter)
    lam=0.5,         # Approval penalty strength
    ema_decay=0.99,  # Loss baseline decay
)

Key parameter: k

• k=0.10 → ~70% acceptance (loose gate)
• k=0.25 → ~60% acceptance (default, balanced)
• k=0.50 → ~50% acceptance (strict gate)

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Monitor the gate

gate = EWEGate()

# After training
print(f"Acceptance rate: {gate.acceptance_rate:.1%}")
print(f"Suppression rate: {gate.suppression_rate:.1%}")

# Reset stats between experiments
gate.reset_stats()

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Available loss functions

from ewe import GCELoss, LabelSmoothingLoss

# Generalised Cross-Entropy (Zhang & Sabuncu 2018)
criterion = GCELoss(q=0.7)

# Label Smoothing (Szegedy et al. 2016)  
criterion = LabelSmoothingLoss(smoothing=0.1)

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Citation

If you use EWE in your research please cite:

@article{purohit2026ewe,
  title={Epistemic Weight Engine (EWE): A Framework for Signal-Reliability-Weighted
         Learning in Artificial Neural Systems, with Multi-Dataset Experimental Evaluation},
  author={Purohit, Maheep},
  journal={ACM Transactions on Intelligent Systems and Technology},
  year={2026},
  note={Manuscript ID: TIST-2026-03-0289},
  doi={10.5281/zenodo.18940011}
}

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Paper and Code

• Paper (preprint): https://doi.org/10.5281/zenodo.18940011
• Experiments: https://github.com/maheeppurohit/epistemic-weight-engine
• Contact: [e-mail]

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Author

Maheep Purohit Independent Researcher, Bikaner, Rajasthan, India Patent Applicant: Adaptive Intelligent Pipeline Integrity System (filed 2025)

This research was conducted entirely independently without institutional affiliation, laboratory access, external funding, or academic supervision.