# DANTE v6 Initial-Region Surrogate Failure Analysis

## Scope

- initial_samples: 100
- dims: 42
- note: analysis uses only init phase, not acquisition samples

## Data Geometry

- rank_ratio: 1.000
- unique_ratio_rounded_0p05: 1.000
- cov_condition_number: 1.53e+01
- pca_components_for_90pct_var: 30
- avg_nn_dist: 4.1265

## Classical Baselines

- ridge_std: r2_mean=-1.0772 ± 0.4586, mae_mean=6.0836
- knn_std_k5: r2_mean=-0.2933 ± 0.2589, mae_mean=4.9774
- rf_300: r2_mean=-0.2074 ± 0.2602, mae_mean=4.6365

## TensorFlow Models

- tf_device: GPU:1
- tf_v4_like_no_scaling: r2_mean=-1.8222 ± 0.8408, mae_mean=7.1709
- tf_v4_like_with_scaling: r2_mean=-0.7289 ± 0.2800, mae_mean=5.9893
- tf_mid_128_64_32_with_scaling: r2_mean=-0.6206 ± 0.4865, mae_mean=5.6784

## Recovery/Noise Signal

- recoveries_ratio_ge1: 0.880
- mean_scaled(recover>=1): 17.945237696501337
- mean_scaled(recover=0): 15.966237587414065
- std_scaled(recover>=1): 4.7475266358578665
- std_scaled(recover=0): 9.213494585712375

## Temporal Drift

- corr(index, y): -0.1209
- forward RF on x: r2=-0.0394, mae=5.0576
- forward RF on [x,index]: r2=0.0153, mae=4.9602
- forward ridge on index only: r2=0.0023, mae=4.9557

## Diagnosis

- Even non-neural baselines fail on init set: weakly learnable mapping or high label noise.
- Input/output scaling is a first-order factor: original no-scaling setup underfits init region.
- Model capacity/optimization also matters after scaling (mid_128_64_32 > v4-like).
- Most init samples require recovery reset; this indicates environment-transition induced label noise in init pool.
- Adding sample index improves forward prediction, indicating temporal/state drift beyond static x->y mapping.