# kaal:claim:4855607-001

**Claim.** Web3 community governance built on Weighted Directed Acyclic Graphs, validation pools with reputation staking, and a federated communications protocol provides an evolutionary approach to optimizing AI models rather than a static compliance layer over them.

**Type.** design  **Support.** argued

**Holds when.**

- applies to the six AI model families the author examines: deep learning, federated learning, transformer, GNN, RL, and RLHF

**Source quote.**

> The integration of web3 community governance, using Weighted Directed Acyclic Graphs (WDAGs) and validation pools with reputation staking in combination with a federated communications protocol, offers an evolutionary approach to AI model optimization.

**From.** Wulf A. Kaal, *How AI Models are Optimized Through Web3 Governance* (2024), Abstract, page 1

**Cite as.** Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607

**Verify.** sha256 of source PDF `eb0b3e62374b45a8fa888c6bde9725e606bcb46cf4b5e74a6e851d9f25099113` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20How%20AI%20Models%20are%20Optimized%20Through%20Web3%20Governance.pdf

**Topics.** governance-design, ai-and-agents, reputation

**Keywords.** web3-governance, ai-model-optimization, wdag, validation-pools, reputation-staking

**Related claims.**

- extended_by: https://wulfkaal.github.io/claims/4957318-031

**Canonical form.** This markdown file is the canonical hashed representation of the claim. Its sha256 is the content hash used for attestation.