Kaggle Submission Instructions

Current Status: LB 6059.08 (V25), Target: 6100

How to Submit (Kaggle Notebook)

# In a Kaggle notebook with neurogolf-2026 competition data:

!pip install onnx onnxruntime numpy

# Clone repo
!git clone https://huggingface.co/rogermt/neurogolf-solver /kaggle/working/neurogolf-solver

# Run swap_and_submit
import os
os.chdir('/kaggle/working/neurogolf-solver/medal-solvers')

!python swap_and_submit.py \
    --base /kaggle/working/neurogolf-solver/submission-6043.zip \
    --task-data-dir /kaggle/input/competitions/neurogolf-2026 \
    --output /kaggle/working/submission.zip

What swap_and_submit.py does:

Opens submission-6043.zip (base submission)
Auto-discovers ALL .onnx files in optimized/ folder:
- optimized/task084.onnx (~12 pts, +2.56 from base)
- optimized/task153.onnx (V4: 14.0 pts local, fixes Kaggle discrepancy)
- optimized/task255.onnx (11.570 pts, +4.93 from base)
- optimized/task319.onnx (11.580 pts, +3.66 from base)
Validates each model against task data (0 failures required)
Creates new submission.zip with replaced models

Task 153 V4 — Key Change

V4 eliminates the multi-dimensional Gather [9,3,3] pattern that caused the Kaggle scoring discrepancy (13.83 local vs 7.92 Kaggle in V3).

Expected impact: If Kaggle matches local, task153 goes from ~7.92 → ~14 = +6 pts

Score Budget (with V4)

Task	Base	V25 LB	V4 Expected	Expected Gain vs V25
084	~9.4	~12	~12	—
153	4.91	~7.92	~14.0	+6.0
255	6.64	11.57	11.57	—
285	5.98	8.82	8.82	—
319	7.92	11.58	11.58	—
Total Expected		6059.08	~6065	+6

Remaining Gap After V4: ~35 pts to 6100

Need to optimize 5-7 more tasks at +5 pts each to reach 6100. Priority targets: task366, task025, task158, task118, task209.

Important Notes

task285.onnx is NOT in the optimized/ folder — it was already included in the original V13 submission. The base zip (submission-6043.zip) already uses the optimized task285.
Always validate before submitting: a model with ANY failures scores only 1.0!
File size limit per model: 1,509,949 bytes