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PRED-01NEGATIVE

Forward prediction of early hash outcomes

This experiment tested whether early SHA-256-derived features could rank candidates before completing the registered evaluation. Validation AUC was approximately 0.5025 and held-out test AUC approximately 0.4998, with lift near 0.978. The hypothesis was closed as negative.

Published
2026-08-14
Updated
2026-08-14
Authors
BTC PoW Lab
Replication
Validation and held-out test completed
Mining advantage claimNO PREDICTIVE ADVANTAGE DEMONSTRATED

Local structure, statistical signal and operational advantage are evaluated separately.

Question

Can early-computation features distinguish later favorable candidates better than chance?

Hypothesis

A stable early feature may provide out-of-sample ranking power.

Why this matters

A real early discriminator could justify conditional work; a failed model prevents wasted engineering.

Method

Train on the discovery partition, freeze the model, then evaluate validation and untouched test partitions.

Experimental setup

Baseline
Random discrimination: AUC 0.5 and lift 1.0.
Setup
Separated discovery, validation and held-out test cohorts.
Hardware
Classical analysis pipeline.
Dataset
Frozen feature matrices and split manifests; release review pending.

Results

Validation AUC≈0.5025
Test AUC≈0.4998
VerdictNEGATIVE
  • Validation AUC ≈ 0.5025.
  • Test AUC ≈ 0.4998.
  • Lift ≈ 0.978.
ANALYSIS / MODEL

Scientific analysis

PRED-01 asks whether information available before the registered full evaluation can rank later outcomes. The model was trained only on discovery data, frozen, and then evaluated on validation and untouched test partitions to prevent adaptive leakage.

Area under the ROC curve has a probabilistic interpretation: for a randomly drawn positive and negative example, AUC is the probability that the model ranks the positive higher, with half credit for ties. Random ranking has expectation 0.5.

Validation AUC 0.5025 did not reproduce as useful discrimination: held-out AUC was 0.4998 and lift was 0.978. These values are operationally indistinguishable from chance for the registered decision rule, so no conditional-work engine was opened.

ROC chart with model curve overlapping the random diagonal and AUC near one halfHeld-out ROC behavior lies on the random diagonal. Validation and test AUC values remain near 0.5, and the selected-region lift is below 1.HELD-OUT ROCFALSE-POSITIVE RATETRUE-POSITIVE RATERANDOM BASELINEAUC 0.4998LIFT0.978≈ RANDOM
Held-out prediction
AUC0.4998
Lift0.978
≈ RANDOM PERFORMANCEROC follows the null diagonal
FIG / EVIDENCEHeld-out ROC behavior lies on the random diagonal. Validation and test AUC values remain near 0.5, and the selected-region lift is below 1.
ANALYSIS / EQUATIONS

Mathematical formulation

AUC = P(s⁺ > s⁻) + ½P(s⁺ = s⁻)

AUC measures ranking, not calibration and not mining throughput.

AUCtest − 0.5 ≈ −0.0002

The untouched test result is essentially on the random-discrimination null.

lift = precisionselected / prevalence = 0.978

The selected region contained slightly less target incidence than a random selection of equal size.

ANALYSIS / STATISTICS

Statistical reading

Discovery performance is not evidence after feature and model selection; the untouched test partition is the decision-bearing result.

AUC near 0.5 can coexist with small local fluctuations. Promoting a post-hoc threshold would require a new preregistration and a new held-out cohort.

The negative conclusion is scoped: it rejects the registered feature/model family, not every conceivable early SHA-256 feature.

ANALYSIS / SCOPE

Validity and scope

  • Split integrity is the main defense against leakage.
  • The model did not meet a predictive or operational opening gate.
  • No statement is made about unregistered nonlinear models or different labels.
SOURCES / METHOD

Methodological references

  1. DeLong et al. (1988) — Nonparametric analysis of ROC areas
  2. NIST FIPS 180-4 — Secure Hash Standard

Interpretation

The results are compatible with random discrimination. No predictive advantage was demonstrated.

Limitations

  • The conclusion applies to the registered feature family and model.
  • It does not prove that every possible early feature is uninformative.

Reproduction

Use the same split discipline and report untouched test performance; discovery-set performance alone is insufficient.

Artifacts

Model card and split manifest are queued for publication review.

Research-use notice

Experimental content is provided for research and educational use, without warranty. Validate independently before relying on it. Read the full disclaimer.