Timeline & Prizes

Schedule & rewards

A three-month development phase, a blind final, and recognition that goes well beyond the cash awards.

Schedule

Key dates

  1. Feb 2026 Complete

    Dataset assembly and curation

  2. Mar 2026 Complete

    Baseline code, evaluation scripts, dFL visualizer

  3. Apr 2026 Complete

    Website online

  4. Jun 2026 Complete

    Public release of training & validation data

  5. Jun – Sep 2026

    Phase 1 (development) — ~3 months, continuous public leaderboard

  6. Sep 2026

    Release of non-blind test data

  7. Oct 2026

    Release of blind test data; Phase 2 (final) opens

  8. Late Oct 2026

    Final phase closes

  9. Nov 2026

    Verification, peer review of top entries, prize allocation

  10. Dec 2026

    NeurIPS competition session; analysis paper draft

  11. Q1 2027

    Lessons-learned community paper with selected winners

Cash awards

$1,000 across two awards

Award #1 $500

Best DIII-D intra-machine reconstruction

Highest composite S_model on the hidden DIII-D test set.

Award #2 $500

Best DIII-D → MAST cross-machine generalization

Highest G_ratio among entries that pass the harmonization quality gate and reach R²ψ > 0.6 on DIII-D.

Four honorable mentions

Top flux fidelity (R²ψ)
Top mean scalar fidelity (R²ₛ)
Top single-scalar R² (e.g. highest R²βN)
Best LCFS alignment (D_LCFS)
Beyond the cash

Recognition & support

Co-authorship & talks

Top three teams in each award category receive named co-authorship on the lessons-learned paper plus an invited podium talk at the NeurIPS competition session.

Certificates & talks

The four honorable-mention teams receive certificates and podium talks.

Compute for everyone

Hugging Face GPU credits and cloud-compute vouchers for participants from underrepresented or resource-constrained institutions.

Accessible by design

The awards are reachable without dedicated hardware: the compute-light recognition celebrates entries that train on a single commodity GPU or CPU. See the tracks and the baselines for what that looks like in practice.