Related Work

Token Curated Registries

A Token Curated Registry (TCR) is a decentralized list where the curation of entries is managed by token holders. These holders use their tokens to vote on whether an entry should be included in the list, incentivized by potential rewards for curating accurately. We extend this concept by integrating a crypto-economic game that changes how curation is performed. This game-theoretic approach ensures that the validation process is both rigorous and fair, enhancing the quality and reliability of the curated data.

pump.fun, Derisking, and Bonding curves

Pump.fun uses a bonding curve mechanism to de-risk the process of getting involved. The bonding curve adjusts token prices based on demand, benefiting early participants and reducing the risk for new users. Similarly, we incorporate a bonding curve incentivize early contributions to contribute data. This mechanism ensures that initial participants are adequately rewarded.

Data Quality in AI

High-quality data ensures that AI models can develop robust understandings and make reliable predictions, whereas poor data quality can lead to inaccuracies in learning. FineWeb, developed by Hugging Face, processes data from 96 CommonCrawl snapshots, focusing on high-quality and relevant data while filtering out low-quality content. This approach improves the reliability and efficiency of datasets used in training AI models. We hope to extend the insights gained from FineWeb's advanced filtering and validation techniques to ensure the integrity and relevance of data used in our platform, thus supporting the development of AI capable of conducting rigorous and innovative scientific studies.

Double or Nothing Lawsuits

In exploring innovative mechanisms for dispute resolution and incentives, we draw inspiration from Robin Hanson's "double or nothing lawsuits" concept. This idea proposes a legal system where individuals can gamble their small claims in a legal lottery, escalating stakes until they reach a value worth disputing in court. Adapting this model, our platform allows validators to counter-stake when they doubt a data point's accuracy, doubling stakes each time until it reaches a threshold for final adjudication by a curator. This ensures rigorous scrutiny of data and aligns validators' financial incentives with their confidence in the data's accuracy.

Adversarial Collaboration

Adversarial collaboration, a concept championed by psychologist Daniel Kahneman, involves bringing together individuals with opposing viewpoints to rigorously scrutinize data. Kahneman, renowned for his work on cognitive biases and decision-making, emphasized this approach to mitigate confirmation bias and improve the accuracy of conclusions. By fostering a structured environment for constructive criticism, adversarial collaboration ensures comprehensive analysis and robust conflict resolution.