nips2026-reviewer-artifacts/nips2026-artifacts-3894

Auto-Researching, Not Hyperparameter Tuning — NeurIPS 2026 Anonymous Reproduction Package

This repository is the anonymous reproduction bundle for the NeurIPS 2026 submission "Auto-Researching, Not Hyperparameter Tuning: Convergence Analysis of 10,000 Experiments".

It contains the checkpoints, representative run logs, and precomputed analysis artifacts required to reproduce every headline number in the paper. Real-name hosting will be provided upon acceptance.

Contents

checkpoints/
  champion-vjepa2-deploy/       # Deployed single-model champion (0.910 mAP_ALL on Nexar)
  search-best-vjepa2/           # LLM search-policy best (0.727 mAP on Nexar)
  best-non-vjepa2/              # Strongest non-V-JEPA 2 baseline (Table 3 cross-backbone)
  asr-8b-lora/                  # 8B LoRA adapter (5.30% WER, #1 on Open ASR Leaderboard)
experiments_sample/             # 256 Nexar + 50 ASR run logs sampled from ~3,190 + ~2,643
computed_values/                # JSON artifacts driving all figures and tables
  data/
    anova.json                  # Section 4 ANOVA decomposition
    convergence.json            # Figure 1 search-policy trajectories
    e2e_anova.json              # Section 6 E2E LoRA boundary ablation
    ablation.json               # Obfuscated-names ablation
    cost_efficiency_deep.json   # Appendix cost table
    deployable_analysis/        # Cross-checks for Section 5 (SMAC / LLM test-set results)
    oracle_correction.json      # Sample-size-matched comparison
  results/
    best_model.pt               # Full V-JEPA 2 champion (weights + head)
    soup_best.pt                # Model-soup variant used in ablation

Reproducing the headline numbers

Commands below use a standard torch + huggingface_hub + peft environment. See the requirements.txt in the anonymous code repository (https://anonymous.4open.science/r/nips-2026-submission-1C52) for pinned versions.

Nexar deployed champion — 0.910 mAP_ALL (Section 5)

The champion result of 0.910 mAP_ALL requires 12-view TTA with cv_mix aggregation (alpha=0.95, tuned on public split). See paper Table 6 for the full TTA/aggregation grid; the paper headline is the 5-seed 12-TTA mean 0.9093 ± 0.0026. The cv_mix strategy uses alpha * last_clip + (1 - alpha) * top6_mean to combine temporal position with confidence ranking.

Without cv_mix (i.e., using default mean+max aggregation with 4-TTA), the same checkpoint achieves 0.906 mAP_ALL — this is the 4-TTA baseline, not the deployed champion.

# Download full V-JEPA 2 checkpoint + head
huggingface-cli download nips2026-reviewer-artifacts/nips2026-artifacts-3894 \
    computed_values/results/best_model.pt \
    --repo-type model --local-dir ./ckpts

# Champion: 12-TTA + cv_mix (0.910 mAP_ALL)
python scripts/nexar/eval_e2e.py \
    --checkpoint ckpts/computed_values/results/best_model.pt \
    --tta 12 --aggregation cv_mix
# Expected: mAP_ALL = 0.910 (Public 0.923 / Private 0.900)

# Baseline: 4-TTA + mean aggregation (0.906 mAP_ALL)
python scripts/nexar/eval_e2e.py \
    --checkpoint ckpts/computed_values/results/best_model.pt \
    --tta 4 --aggregation mean
# Expected: mAP_ALL = 0.906

Nexar search-policy ceiling — 0.727 (Table 6)

huggingface-cli download nips2026-reviewer-artifacts/nips2026-artifacts-3894 \
    checkpoints/search-best-vjepa2 --repo-type model --local-dir ./ckpts/search_best
python scripts/nexar/eval_e2e.py --checkpoint ckpts/search_best/best_model.pt --tta 1
# Expected: mAP ≈ 0.727

Cross-backbone transfer (paper Table 4; V-JEPA 2 0.906 vs retrained MViTv2-S 0.692 at matched HPs — per-run data in mvitv2s_matched_hp/)

huggingface-cli download nips2026-reviewer-artifacts/nips2026-artifacts-3894 checkpoints/best-non-vjepa2 \
    --repo-type model --local-dir ./ckpts/non_vjepa
python scripts/nexar/cross_backbone_transfer.py \
    --vjepa ckpts/search_best/best_model.pt \
    --alt ckpts/non_vjepa/best_model.pt \
    --output xfer.json

ANOVA decomposition — primary $\eta^2_{\mathrm{arch}} = 0.74$ (cell-capped, $\omega^2=0.716$; sensitivity range 0.20–0.79) (Section 4)

The ANOVA numbers are fully reproducible from the precomputed JSON without re-running any experiments:

python3 -c "
import json
d = json.load(open('computed_values/data/anova.json'))
print('Nexar eta^2_arch:', d['nexar']['eta_squared']['architecture'])
print('UCF-101 eta^2_arch:', d['ucf101']['eta_squared']['architecture'])
"
# Nexar eta^2_arch: 0.74 cell-capped primary (0.51 adaptive sensitivity)  |  UCF-101 eta^2_arch: 0.148

To reproduce from scratch, run the analysis script from the anonymous code repo against the sampled (or full) experiment logs:

python analyze_predictions.py \
    --experiments experiments_sample/ \
    --output anova_from_sample.json

E2E LoRA boundary ablation — $\eta^2_{\mathrm{arch}} \downarrow 0.04$ (Section 9)

python3 -c "
import json
d = json.load(open('computed_values/data/e2e_anova.json'))
print(d['eta_squared'])
# {'backbone': 0.038, 'encoder': 0.0006, 'backbone_x_encoder': 0.043, 'learning_rate': 0.823}
"

ASR 8B LoRA — 5.30% WER (Section 7)

huggingface-cli download nips2026-reviewer-artifacts/nips2026-artifacts-3894 checkpoints/asr-8b-lora \
    --repo-type model --local-dir ./ckpts/asr_8b
# Merge adapter on base model and evaluate on Open ASR test bundle
python asr_eval.py --adapter ckpts/asr_8b/ --benchmark open_asr
# Expected WER: 5.30%

Experiment log sample

experiments_sample/ contains 150 Nexar + 50 ASR runs sampled uniformly at random (seed 20260421) from the full campaign. Each run directory contains:

• idea_config.yaml — the configuration proposed by the agent
• metrics.json — the resulting test-set metrics
• claim.json — the agent's rationale (where logged)

This sample is sufficient to independently re-run the ANOVA script and verify the variance decomposition to within sampling error. The complete ~10,000-run campaign log will be released upon acceptance.

Citation

The paper is under double-blind review; citation metadata will be added upon acceptance.

License

Apache 2.0. Third-party model weights (V-JEPA 2; the publicly released ASR base model, name withheld for double-blind review) remain subject to their original licenses.