TRACK DETAILS

Track Details

Explore the four tracks, challenge requirements and participant support.

Global Open Source RMB 5M Prize Pool

AI for Research

Overview

Focus on the intersection of AI and science, build runnable algorithmic solutions or autonomous exploration environments around real scientific problems, and make AI an effective tool for scientific discovery.

This track focuses on the intersection of AI and science. It encourages participants to build runnable algorithmic solutions or autonomous exploration environments around real scientific problems, making AI an effective tool for scientific discovery.

The track is not only about single-point model performance. It focuses on how to turn scientific problems into computable, verifiable, and reproducible exploration tasks, forming a complete loop from problem definition and environment design to algorithm execution and interpretation of research signals.

Prize Information

GOAI Grand Award1 team / RMB 1M

*The winner of the Grand Award will be selected from the four track champion teams.

Champion2 teams / RMB 250K each
Runner-up2 teams / RMB 150K each
Third Prize2 teams / RMB 50K each

Additional Awards

Scientist Fast-track Award1-2 teams, RMB 20K each, directly nominated by scientist judges and independent from champion, runner-up, and third-prize awards.
Paradigm Creation Award0-1 team, RMB 20K, for convincing dynamic scientific evaluation frameworks or exploration paradigm designs.
Excellence AwardSemi-finalist teams receive a commemorative gift package worth RMB 500.
Participation AwardTeams submitting valid entries receive a competition T-shirt.

Question Types

Type 1: Algorithmic Problems

For scientific problems with clear evaluation methods, participants use datasets and evaluation frameworks provided by the organizing committee to model and solve the task, forming runnable and reproducible algorithmic solutions. Initial fields include virtual cells, small-molecule protein binding trajectory prediction, and materials science literature-driven scientific discovery agents.

Type 2: Open Exploration Problems

For important scientific problems that have not yet been structured as standard evaluation tasks, participants are encouraged to define their own problems, exploration environments, and discovery signals, turning scientific intuition into an operating loop that agents can continuously explore.

Track Claim: Let AI Advance Scientific Discovery on Real Problems

Encourage teams to turn scientific problems into computable, verifiable, and reproducible exploration environments and algorithmic solutions. Negative results are allowed, but the process must be explainable, inspectable, and extensible.

Core Work Requirements

Core Work Requirements

  • Entries may choose either algorithmic problems or open exploration problems, and should form runnable, reproducible, and verifiable solutions or exploration environments around real scientific problems.
  • Entries should clearly disclose problem definition, evaluation or discovery signals, data sources, dependent tools, runtime flow, reproducibility method, and open-source plan. Open exploration entries also need to explain the minimum reference baseline and exploration log design.
  • The preliminary round focuses on problem value, technical feasibility, and open-source potential. The semi-final requires a runnable environment or final code, technical documentation, and experiment / exploration results. The final focuses on live explanation, research signals, and long-term research potential.

Submission Requirements

Detailed submission requirements are as follows:

StageTimeEvaluation FocusSubmission Content
PreliminaryJul 15-Aug 16Problem value, technical feasibility, and open-source potentialExploration: problem definition document (4 pages); Algorithmic: preliminary solution and interim experiment results
Semi-finalAug 25-Sep 23Runnability, research signals, reproducibility, and standardsExploration: complete three-piece set; Algorithmic: final code + complete experiment results
FinalSep 22-Sep 23Live presentation, scientific interpretation, and long-term potentialExploration: final pitch deck, on-site demo, one-page project summary, and defense materials; Algorithmic: poster + showcase materials

Review Focus

Projects will focus on the following areas:

  • Problem definition and scientific value
  • Exploration environment / evaluation framework design
  • Runnability, reproducibility, and evidence quality
  • Exploration process and research signals
  • Open contribution and long-term research potential

Participant Support

This track plans to provide teams that complete preliminary submission and pass submission validity review with up to RMB 200 in compute, cloud services, and other development resource subsidies, or equivalent competition support resources, with a quota of up to 300 slots in principle. After the preliminary submission deadline, the organizing committee will, based on submission and review progress, notify eligible teams via email or official community channels to submit relevant materials, and distribute resources in an orderly manner according to the rules. Resource support is not a competition prize, review bonus, or advancement criterion. Specific support forms, application conditions, material requirements, distribution methods, and schedule are subject to subsequent notices from the organizing committee.

Best-fit Teams

  • This track is suitable for interdisciplinary teams with AI / ML capabilities and real understanding of scientific fields such as biology, chemistry, materials, astronomy, and physics, as well as PhD students, postdoctoral researches, young PIs, university labs, and open-source teams hoping to explore AI solutions around unsolved scientific problems.

Registration & Resources