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LUCKY LOOP

World Model · Predicts experiment outcomes before compute

Current State

State ID

open-does-feature-scaling-improve-logistic-regression-accuracy-on-breast-canc

Dataset

eeg_eye_state

Sources

Sources ingested · 6 papers · 1 claims extracted

Policy scores (softmax, T=0.20) rank candidate pipelines before compute. CV accuracy reported as repeated 5-fold mean with 95% CI. Every claim is gated by an effect-vs-noise verifier.

Predicted Next States

1

randomforestclassifier

s_10227D8

policy p

0.53

CV acc 95% CI 0.92–0.93BEST CANDIDATE
2

gradientboostingclassifier

s_0312B3B

policy p

0.29

CV acc 95% CI 0.80–0.81OFF-POLICY
3

logisticregression

s_65D7D31

policy p

0.09

CV acc 95% CI 0.52–0.62LOW CONFIDENCE
4

svc

s_A4D1586

policy p

0.08

CV acc 95% CI 0.47–0.65
VERDICT

EFFECT EXCEEDS NOISE

Δacc 0.121 > seed std 0.0113

The measured effect exceeded seed noise by the support threshold.

AUTO-RESEARCH PIPELINE

Literature → Predict → Run → Verify → Report

  1. 1

    SCOUT arXiv

    Crawl recent papers

    QUEUED
  2. 2

    FETCH PAPERS

    Download metadata

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  3. 3

    EXTRACT CLAIMS

    Parse method + metrics

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  4. 4

    PREDICT

    Qwen-AgentWorld · before compute

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  5. 5

    RUN EXPERIMENT

    real sklearn

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  6. 6

    CROSS-CHECK

    verifier · effect vs noise

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  7. 7

    REPORT

    verdict + provenance

    QUEUED

Run Log (Live)

∴ idle — press RUN to wake the oracle…

FINDINGS

measured results · lucky-loop