The Future of Preprints Is Permanent and Forkable

Decentralized reputation systems like DeSci Rep or ResearchHub become prime targets. Attackers can fork a preprint, create thousands of fake peer reviews, and artificially inflate credibility to push fraudulent or low-quality work.

• Undermines Trust: Legitimate peer review is drowned out by noise.
• Economic Incentive: Bad actors profit from manipulating tokenized reputation or grant distributions.

Permanence is a double-edged sword. A foundational paper with a critical, discovered error cannot be 'unpublished'. While forks can correct it, the original flawed version persists forever, polluting the citation graph.

• Citation Hell: Future work may inadvertently cite the erroneous version.
• Fork Proliferation: The canonical record fragments into competing corrected versions, confusing the field.

Forking a preprint and minting it as a new NFT could violate copyright, especially for work later published in traditional journals. Platforms like arXiv have licensing terms that forking may breach.

• Legal Liability: Researchers and platforms face cease-and-desist orders.
• Chilling Effect: Institutions may ban researchers from using permanent preprint networks.

Every minor revision, comment, and fork is stored on-chain or in decentralized storage like Arweave or IPFS. The system incentivizes volume over quality, leading to unsustainable data growth.

• Cost Explosion: Storing petabytes of low-value forks becomes economically prohibitive.
• Search Collapse: Finding signal in the noise becomes computationally intractable.

Protocols that use token voting (e.g., DeSci DAOs) to curate or fund research are vulnerable. Large token holders (whales) can dictate which research lines are promoted, replicating and automating the gatekeeping of traditional academia.

• Centralization: Decision-making power concentrates, defeating decentralization goals.
• Ideological Bias: Research agendas are set by capital, not scientific merit.

Automated smart contracts for bounty payouts or replication rewards require oracles to verify real-world outcomes (e.g., 'was this experiment successfully replicated?'). Oracles like Chainlink become single points of failure or manipulation.

• Trust Assumption: Shifts trust from reviewers to oracle operators.
• Manipulable Outcomes: Bad actors can corrupt the oracle to falsely claim success and collect bounties.

Centralized journals extract value without adding it, creating ~12-month publication delays and paywalls that lock away knowledge. The system is optimized for rent extraction, not truth discovery or speed.

• Value Capture: Publishers capture value from public funding and author labor.
• Inefficient Discovery: Novel research is siloed and slow to propagate.
• No Composability: Data, code, and findings are trapped in static formats.

Store the canonical research object—manuscript, data, code—permanently on Arweave. Attach smart contracts (on Ethereum, Solana, etc.) to manage attribution, royalties, and governance. This creates a forkable knowledge base.

• Permanent Storage: Data is immutable and globally accessible, akin to IPFS but with perpetual funding.
• Native Monetization: Authors can embed royalty streams via Superfluid or similar.
• Fork as Collaboration: Anyone can permissionlessly fork and build upon the work, creating verifiable lineage.

A preprint is no longer a document; it's the seed for a DAO. Contributors (reviewers, data analysts, replicators) earn tokens or reputation (SourceCred, Coordinape) for verifiable work. Funding (Gitcoin Grants, MolochDAO) is programmatically tied to milestones.

• Merit-Based Funding: Retroactive Public Goods Funding models directly reward impactful work.
• Trustless Collaboration: Contributions are on-chain, reducing credentialism.
• Liquid Reputation: Contributor stakes and reputation are portable assets.

Replace opaque, slow peer review with a staked, time-bound prediction market (e.g., Polymarket-style). Reviewers stake on a paper's validity or impact. Accurate predictions earn rewards; faulty ones lose stake. This aligns incentives for rigorous review.

• Skin in the Game: Reviewers are financially incentivized to be correct, not just critical.
• Signal Over Noise: Market odds provide a clear, crowdsourced quality score.
• Automated Execution: Smart contracts auto-distribute royalties based on market outcomes.

Follow the modular blockchain ethos. Arweave for permanent storage. Ethereum or Solana for high-value logic (royalties, DAO governance). IPFS or Ceramic for mutable metadata. Farcaster-like clients for discovery. This prevents vendor lock-in and ensures upgradeability.

• Resilience: No single point of failure.
• Specialization: Each layer uses the optimal tech stack.
• Permissionless Innovation: Anyone can build a new interface or analytics tool on the open data layer.

The value accrual shifts from publishing brands to the protocols and primitives that enable the new system. This mirrors the shift from MySpace to Facebook to the Lens Protocol. Invest in the infrastructure for permanent, composable knowledge.

• Protocol Cash Flows: Fees from minting, forking, and royalty distribution.
• Data Layer Moats: The permanent graph of forked research becomes an unassailable asset.
• Vertical Integration: Own the stack from storage (Arweave) to discovery engines.