What is yesnoerror (YNE)?

Deep Dive

1. Purpose & Value Proposition

YesNoError addresses systemic flaws in scientific peer review by automating error detection in studies. It targets three error types:
- Methodological (e.g., biased sample selection).
- Statistical (p-hacking, overfitting).
- Interpretational (false causation, overgeneralization).

The platform gained traction after high-profile incidents, like a 2024 study overstating toxic chemical risks in household plastics, where AI identified critical discrepancies in minutes.

2. Technology & Architecture

The system uses a multi-agent AI framework to dissect research papers:
- Synthetic Data Pipeline: Injects known errors into real studies to train detection models.
- Chunk-Based Analysis: Splits papers into 1,000-token segments for efficient processing via retrieval-augmented generation (RAG).
- Specialized Reviewers: Agents like Math Checker and Logic Checker analyze specific aspects, aggregating findings into structured reports.

3. Tokenomics & Governance

The $YNE token enables:
- Community-Driven Audits: Token holders vote to allocate resources (e.g., prioritizing cancer or AI ethics research).
- Token Burns: A portion of audit revenue buys and burns tokens, reducing supply.
- Incentive Alignment: Combats the "public good problem" by rewarding participation in large-scale verification efforts.

Conclusion

YesNoError merges AI rigor with decentralized governance to create a self-sustaining ecosystem for scientific integrity. By democratizing access to tools once reserved for billion-dollar labs, it aims to rebuild trust in research. Could this model eventually expand beyond academia to verify legal, financial, or policy documents?