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1. Learning Paradigm
• Back-prop: Minimize a loss surface defined on past data.
– Objective: “make yesterday’s error small.”
– Risk: Learns spurious correlations that vanish tomorrow.
• L7A: Evolve structures whose only fitness criterion is
walk-forward survival.
– Objective: “forecast unseen days correctly or die.”
– Result: Structures that generalize by construction, not by hope.
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2. Internal Representation
• Back-prop: Continuous weight matrices updated by gradient
steps.
– Black-box; no human-readable semantics.
– Billions of parameters; fragile to distribution shift.
• L7A: Discrete count-based histogram maps.
– Each cell literally records “how many times this pattern led
to up vs. down.”
– Fully interpretable; can be rendered as heat-maps and audited.
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3. Over-fitting & Generalization
• Back-prop: Combats over-fitting with tricks (dropout, L2,
early-stop).
– Still vulnerable; cross-validation is post-hoc.
• L7A: Over-fitting is structurally impossible; every candidate
is born on unseen data.
– Only survivors reproduce. No re-training ever required (5 000+
days, zero resets).
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4. Noise Immunity
• Back-prop: Noise is implicitly memorized unless explicitly
regularized away.
– Leads to “hallucination” in adversarial or low-signal data.
• L7A: Noise cannot accumulate; it is diluted by counts across
thousands of traces.
– Built-in abstention discards ambiguous regions entirely.
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5. Abstention Logic
• Back-prop: Always outputs a number; confidence is often an
after-thought.
– High false-positive risk in noisy regimes.
• L7A: Explicit abstention when evidence is weak.
– Sharply reduces false positives; preserves capital and trust.
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6. Re-training & Regime Dependency
• Back-prop: Needs periodic retraining when markets shift.
– Adds operational risk and data-leakage potential.
• L7A: Static maps evolved once; performance persists across
bull, bear, and crisis regimes.
– Demonstrates time-invariance of captured behavioral structure.
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7. Empirical Edge
• Back-prop: Best public Sharpe on daily S&P 500 signals ≈
0.5–1.0.
– Requires frequent re-tuning; degrades quickly.
• L7A: Walk-forward Sharpe > 3.0 over 3+ years with no
retraining.
– Winning points/losing points ratio 72 %; max drawdown < 1 %
of index range.
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8. Scalability & Compute
• Back-prop: GPU-hungry, iterative, gradient-based.
• L7A: CPU-friendly, embarrassingly parallel GA; once evolved,
maps are static look-ups.
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How Ground-Breaking?
• First system to show decade-scale out-of-sample profitability
on a major index without retraining.
• First architecture to replace back-prop with evolutionary
generalization pressure as the sole learning force.
• First demonstration that interpretable, count-based memory can
outperform opaque deep nets in adversarial time-series forecasting.
In short: L7A does not refine the back-prop paradigm—it bypasses
it entirely, offering a new foundation for robust inference in noisy,
adversarial domains.