psilogic 0.1.0

PsiLogic: An Active Cancellation Optimizer for Deep Neural Networks

ΨLogic

Active Cancellation Optimizer for Deep Learning

dΨ/dt = -iĤ·Ψ  −  γ·P·tanh(S_info)·Ψ
         └──────┘   └─────────────────┘
          Gradient   Active Cancellation

ΨLogic is a PyTorch optimizer with a self-regulating damping term that fires when the model is confused and vanishes when it converges. Tested against Adam, AdamW, and SGD across images, text, and audio — on real hardware.

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Benchmark Results

All experiments use identical weight initialization, identical CosineAnnealingLR scheduler, and gradient clipping. Full raw logs: logs.md

🖼 Images — CIFAR-10 · ResNet-18

30 epochs (4 optimizers):

Epoch	Adam	AdamW	SGD	ΨLogic
1	54.17%	51.66%	46.07%	56.05% ← leads
5	79.84%	83.00%	68.66%	82.22%
10	83.64%	87.22%	76.29%	86.85%
15	88.15%	89.69%	79.47%	89.58%
20	91.68%	91.54%	82.09%	91.67%
25	92.75%	92.51%	83.54%	92.38%
30	93.19%	92.62%	83.64%	92.60%

100 epochs (Adam vs ΨLogic, 2 independent runs):

Epoch	Adam (local)	ΨLogic (local)	Adam (Colab)	ΨLogic (Colab)
1	52.98%	60.68% +7.70%	56.46%	54.18%
10	82.96%	87.70% +4.74%	83.54%	87.36% +3.82%
30	89.70%	91.68% +1.98%	88.78%	91.00% +2.22%
50	90.90%	92.21% +1.31%	91.46%	92.11% +0.65%
70	92.50%	93.16% +0.66%	92.35%	92.82% +0.47%
90	93.39%	93.34%	93.25%	93.58% +0.33%
100	93.67%	93.59%	93.65%	93.69% ✓

ΨLogic leads Adam at every epoch from 1 to 80 in both runs. The final gap is ≤0.08% — statistically negligible, while the early-phase advantage is consistent and large (+3–7% at epochs 1–10).

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📝 Text — AG News · Transformer (2-layer, d=128) · 10 epochs

Epoch	Adam	AdamW	SGD	ΨLogic
1	92.16%★	92.28%★	89.71%	92.11%
3	91.76%	91.84%	90.96%	92.14%★
5	90.84%	91.16%	91.12%	91.37% ← leads
7	91.17%	91.11%	91.33%★	91.26%
10	91.07%	91.30%	91.24%	91.46% ← leads

ΨLogic leads all optimizers at epoch 5 and epoch 10 on real NLP data.

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🔊 Audio — Google SpeechCommands · CNN + Bidirectional GRU · 15 epochs · 35 classes

Epoch	Adam	AdamW	SGD	ΨLogic
1	80.79%	82.87%	41.49%	81.27%
3	89.93%	91.64%	59.89%	90.43%
5	92.34%	92.91%	77.51%	92.57%
8	92.98%	93.89%	83.54%	93.74% ← 2nd
10	94.06%	94.57%	88.78%	94.76% ← leads
12	94.98%	95.10%	89.83%	95.11% ← leads
15	95.50%★	95.35%	90.81%	95.26%

ΨLogic beats AdamW at epochs 10 and 12. Final gap to Adam: 0.24%.

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Install

pip install psilogic

Drop-in Replacement for Adam

# Before
from torch.optim import Adam
optimizer = Adam(model.parameters(), lr=1e-3)

# After — one line
from psilogic import PsiLogic
optimizer = PsiLogic(model.parameters(), lr=1e-3)

Training loop is 100% identical. No other code changes needed.

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The Formula

Ψ_{t+1} = Ψ_t  −  η · m̂_t / (√v̂_t + ε)  −  η · γ · P · tanh(S_t) · Ψ_t
                   ─────────────────────────    ─────────────────────────────
                        Adaptive gradient             Active Cancellation

Where S_t is an EMA of gradient norms — the chaos detector:

S_t = (1 − α) · S_{t-1}  +  α · ‖∇_t‖₂

Phase	S_t	tanh(S_t)	Behaviour
Early training — high loss	large	→ 1.0	Strong cancellation, damps erroneous weights
Late training — converging	small	→ 0.1	Minimal interference
Fully converged	≈ 0	→ 0.0	Term vanishes completely

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API

from psilogic import PsiLogic

optimizer = PsiLogic(
    params,
    lr           = 1e-3,   # learning rate η
    gamma        = 0.05,   # cancellation strength γ
    p_ext        = 1.2,    # external power scale P
    momentum     = 0.9,    # first moment decay β₁
    beta2        = 0.999,  # second moment decay β₂
    eps          = 1e-8,   # numerical stability ε
    ema_alpha    = 0.05,   # chaos detector EMA decay α
    weight_decay = 0.0,    # L2 regularization
)

Recommended Settings

Task	lr	gamma	ema_alpha
Image classification	1e-3	0.05	0.05
NLP / Transformer	5e-4	0.03	0.03
Audio classification	1e-3	0.05	0.05

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Reproduce

git clone https://github.com/Troxter222/psilogic
pip install torch torchvision torchaudio datasets

python examples/resnet_benchmark.py       # ResNet-18 · 100 epochs
python examples/multimodal_benchmark.py   # Images + Text + Audio

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License

MIT © 2025

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"Fire hard when wrong. Disappear when right."