4/4.🛑 Problem 3: Absence of Trust Infrastructure.Core issue: No "trust layer" to verify contributions/coordinate effectively. Critical gaps:.• Unverifiable contributions (can’t confirm if data improved models).• Opaque governance (no stakeholder consensus framework).•.

3/4.⚖️ Problem 2: Contribution-Incentive Misalignment. Core issue: Systems fail to quantify/reward long-tail contributions. Structural flaws:.• Open-source exploitation (researchers lose $ after sharing data/models).• Long-tail paradox (e.g., tropical disease data undervalued).

🔬 Problem 1: Fragmented Ecosystems.Core issue: Research occurs in isolated silos (academia/corporate R&D/closed-source). Manifestations:.• Domain isolation (e.g., biomed + climate efforts disconnected).• Technical incompatibility → Blocks modular workflows.• Reproducibility.

Why Scientific Research & Web3 Coordination Are Broken. 1/4.🚫 AI advances, but scientific research & Web3-native coordination face deep structural flaws:. • Siloed data.• Fragmented ecosystems.• Poor attribution.• Misaligned incentives. → Creates a bottlenecked innovation

🌐 And it’s cross-platform:. - Datasets and models don’t just live inside ModelStation. Any CIP-integrated project can access and build on them. - We're excited to help shape a fairer, interoperable AI ownership layer. Stay tuned. #AI #DeAI #CIP #ModelStation #OpenScience.

🔑 Every action (uploading a dataset, fine-tuning, launching agents) mints a Key—a DID that proves and records your contribution to the ecosystem. These Keys aren't just points. They're causally linked:.If others use your fine-tuned model, your contribution gains weight—not just.

🚧 We're building ModelStation, a project integrating into the CIP (Causal IP) ecosystem. CIP redefines ownership in AI: it tracks datasets, fine-tuned vertical models, and agent-level contributions via an open, composable standard. 🧠📚. 🧩 Here’s how it works:. - Anyone can

@openscience Radical reimagining:.Data exists in quantum value-states:. -Raw behaviors = value superposition.- Measurement (access) = price eigen-collapse.- Entanglement = citing paper boosts review value.

2/3 Nash equilibrium achieved:.When marginal privacy cost = marginal knowledge value:. math.\frac{\partial U_u}{\partial p_a} = \frac{\partial U_b}{\partial V(k)}. Proof: @OpenScience’s reputation bonds show 83% fewer low-effort reviews when:.Reviewers stake tokens.Payouts ∝.

Behavioral Markets - Ending the Privacy-Value Tradeoff. 1/3 .Web3's impossible choice: Hoard worthless private data OR surrender sovereignty for value. CIP resolves this via Subspace Schelling Games:.- Users sell causal behavioral streams (e.g., paper citations).- Buyers access

Unresolved frontiers:. The Heisenberg Uncertainty of Causality:.Can we measure an event’s influence without perturbing its descendants?.Temporal Quantum Entanglement:.Could AI-predicted future events alter the causal weight of the present?.Join our Causality & Time Working Group:.

Real-world implications:.In OpenScience, this enables:. - Tracing how a flawed dataset propagates through citation networks via causal gradient analysis.- Quantifying a paper’s retroactive impact when later reviews reshape its interpretation.- Mapping "knowledge dark matter":.

(2/5) CIP’s Verifiable Logic Clocks (VLC) answer this by constructing a causal phase space. Each event carries: . - A physical timestamp .- A causal signature: A cryptographic fusion of its parent events’ identities . 🪂This transforms causality from a chain into a topological.

The Relativity of Causality: How CIP Reinvents Temporal Logic. (1/5).We live in a world of entangled causes:.💻A paper (Event A) inspires an experiment (Event B).🚝Meanwhile, its dataset update (Event C) invalidates Event B. Classical blockchains fail this causal stress test with

4/ What can these User Keys do?. These user behaviors are also data in themselves, which can be used to fine-tune the big model of the dataset and become a more professional web3 LLM. The web3 project built on CIP network utilizes User key to achieve a fairer token distribution.

3/ How does CIP empower web3?. Today's web3 projects often require a lot of effort to collect data from users, especially to identify witch users and effective contributors. Based on CIP network, all user behaviors will be recorded, which can later be combined with AI to identify.

2/ How does CIP leverage AI Application？. The above figure shows the architecture diagram of OpenScience Application. All the user's actions are parsed into CIP Event and pushed to the nodes. The user's behavior is called User Key and these User Keys record all the user's.

Introduce OpenScience built on our CIP(Causality Improvement Protocol) system. 1/ What is CIP(Causality Improvement Protocol)?. 1⃣ CIP is an extension based on NIP(Nostr Implementation Possibilities) . User behavior will be recorded in a subspace which we call user key, and each

4/In short: . the DKG turns fragmented knowledge into an interoperable, causally-linked knowledge substrate. It is the dynamic foundation for:. Causal validation (PoCW). AI-driven scientific agents.Provenance & attribution in DeSci.This is why DKG matters. 🧠✨.

3/.✅ Causality-aware by design. Every claim or result is linked to its upstream dependencies. This enables permissionless audit, verification & reuse — critical for both scientific rigor & AI alignment. ✅ Composable & modular. Scientific artifacts can be reused & extended.