Abstract
This proposal outlines a project to build an AI4Science Oracle, a decentralized and verifiable platform that incentivizes the development of AI for science. The platform enables individuals to contribute scientific formulas, findings, or conjectures, which are evaluated for correctness and novelty using formal verification and AI-based novelty assessment. Contributors who meet these criteria are rewarded with tokens. The project aims to accelerate AI’s capabilities in science by systematically integrating novel scientific findings into AI models, fostering a community-driven ecosystem for advancing human and AI collaboration in scientific discovery.
Background
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AI4Science (AI for Science): uses artificial intelligence to accelerate scientific discoveries, model complex phenomena, and solve problems across disciplines like biology, physics, and medicine.
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DeSci (Decentralized Science) leverages decentralized technologies, such as blockchain and DAOs, to make scientific research more transparent, accessible, and community-driven.
Workflow
The AI4Science Oracle operates through a systematic and decentralized workflow, ensuring that contributions are validated, incentivized, and integrated into the broader AI4Science ecosystem. Let’s take AI4Math as an example.The steps are as follows:
1. Submission of Proposals
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Individuals submit their mathematical formulas, findings, or conjectures to the AI4Science Oracle via a decentralized platform.
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Each submission is recorded on the blockchain to ensure transparency and immutability.
2. Validation Phase
The oracle conducts two core tests to evaluate the submissions:
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Correctness Verification:
- The oracle uses formal verification, random testing, and other validation techniques to ensure the correctness of the submitted results.
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Novelty Assessment:
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Leveraging the AI4Science model, the oracle evaluates the “novelty distance” between the proposed result and existing knowledge.
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Submissions that can be easily extrapolated or derived from known formulas fail the novelty test, ensuring only significant contributions pass.
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3. Rewards
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Submissions that pass both tests earn the proposer AI4Science tokens as a reward for their contribution.
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Tokens are minted exclusively for those who contribute validated results, ensuring value is tied to scientific advancement.
4. Challenge Period
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Once a proposal passes the initial validation, it enters a public challenge period.
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During this time, anyone can challenge the proposal by:
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Providing a Counterexample: Demonstrating the incorrectness of the submission.
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Submitting a Formal Proof: Proving the correctness or invalidity of the proposal.
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Successful challengers receive AI4Science tokens as a reward for their contributions to the validation process.
5. Integration into AI4Science Models
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Proposals deemed correct and novel after the challenge period are integrated into the AI4Science model.
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These validated findings are used to improve the AI model’s reasoning, capabilities, and knowledge base, accelerating its ability to solve more complex problems.
6. Decentralized Execution and Verifiability
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All operations, including validation, challenges, and token distribution, are conducted in a decentralized and verifiable manner.
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Technologies such as opML (Optimistic Machine Learning) are employed to ensure trustless and verifiable execution of AI models and validation algorithms.
7. Ecosystem Growth
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By continuously validating and integrating novel findings, the AI4Science Oracle incentivizes contributions and fosters a thriving community of researchers and validators.
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The resulting improvements in AI capabilities further accelerate scientific discovery, creating a positive feedback loop of innovation.
This proposal ensures a robust, transparent, and decentralized system for advancing both human and AI contributions to science.
Conclusion
The AI4Science Oracle bridges the gap between human ingenuity and AI capabilities, offering a decentralized, transparent, and incentivized approach to advancing scientific discovery. By rewarding contributors and challengers and continuously integrating novel findings into AI models, this project not only accelerates the development of AI4Math but also lays the foundation for broader applications across scientific fields.