ResolveAI

[kirandev]

Project Name

ResolveAI

Problem Statement

Disputes in Web3, like failed DeFi trades, NFT scams, or DAO disagreements, are a mess—slow, costly, and often centralized. Users and developers lose time and trust waiting for resolutions that might not even be fair.

Solution Overview

ResolveAI is a slick dApp on Hyperion that uses Alith’s AI to sort out disputes fast, like in seconds, not days. It digs into contract data, transaction logs, and user inputs to suggest fair fixes, all transparent on-chain. With Hyperion’s speed and Metis SDK for cross-chain support, it’s built to scale across DeFi, NFTs, and DAOs. What’s cool is how it automates trust without middlemen, making Web3 feel more reliable.

Project Description

ResolveAI tackles Web3 disputes head-on, whether it’s a botched DeFi loan, a shady NFT sale, or a DAO vote gone wrong. It’s got four big features:

(1) Alith’s AI crunches contract logs, transactions, and user messages to figure out what happened and propose a fix—like a refund or contract tweak.

(2) Hyperion’s parallel processing makes it lightning-fast, handling disputes in under a second.

(3) Metis SDK lets it work across chains, so no dispute gets stuck in one ecosystem.

(4) Every decision is logged on-chain for anyone to check, so it’s all above board. We’ll build it with Solidity for smart contracts and Alith SDK for AI, plus a simple UI for users to submit disputes and see results. Validators can review tricky cases and earn RES tokens. Users get quick, fair resolutions, and we’re stoked to make Web3 trust issues a thing of the past with AI that’s fast and transparent.

Community Engagement Features

Tasks: Submit mock disputes (50 points), validate resolutions (100 points), audit logs (75 points), test cross-chain (150 points).

Points System: RES points redeemable for badges/tokens, tracked on leaderboard.

Gamification: Competitive rankings incentivize testing; mock disputes engage users.

Getting Involved

Community members can join ResolveAI by:

Engaging in the Hyperion Initiators Group (https://forum.ceg.vote) to discuss ideas and provide feedback.

Testing mock disputes on the Hyperion testnet and earning RES points.
Contributing as developers via GitHub (repo TBD) for smart contract or UI enhancements.

Participating in AMAs and workshops to refine features or propose use cases.

Upvoting ResolveAI’s proposal in the Ideathon Category to boost visibility.

[priyankg3]

Hello @kirandev,

1. How does ResolveAI determine “fairness” in a dispute — especially when multiple parties present conflicting perspectives?
2. How is Alith’s AI trained or fine-tuned to analyze disputes from DeFi, NFTs, and DAOs — which all have very different transaction contexts?
3. Is there an appeal or override mechanism if a user disagrees with the AI-generated outcome?

[kirandev]

#1. How does ResolveAI determine “fairness” in a dispute — especially when multiple parties present conflicting perspectives?

ResolveAI ensures fairness by prioritizing verifiable on-chain data (70%), contract rules (20%), and user inputs (10%). Alith’s LLM evaluates transaction logs and rules for objective outcomes. Conflicting perspectives trigger validator review, ensuring impartiality via community consensus, logged on-chain.

2. How is Alith’s AI trained or fine-tuned to analyze disputes from DeFi, NFTs, and DAOs?

Alith’s LLM analyzes disputes using historical dispute patterns from testnet data, contract logs for violations, and multi-modal inputs (transactions, user messages, metadata). It’s fine-tuned with simulated disputes for context-aware resolutions.

3. Is there an appeal or override mechanism if a user disagrees with the AI-generated outcome?

Users can appeal once within 24 hours, submitting new evidence. Alith re-analyzes, validators review, and outcomes are logged on-chain.

Hi @priyankg3, I hope my solutions address your questions about ResolveAI. I’m eager for more feedback or questions to dive deeper and refine the proposal further!

[priyankg3]

Thanks for the all answer @kirandev , Just one question to ask : - How are validators selected and rewarded with RES tokens? Can anyone become a validator, or is it permissioned/staked?

[han]

Thank you for sharing such a forward-thinking and much-needed solution for the Web3 space! ResolveAI addresses one of the most frustrating pain points in decentralized ecosystems with a clever combination of AI, transparency, and automation. It’s exciting to see a project that values both speed and fairness without sacrificing decentralization.

Here are a few thoughtful questions to help understand its broader potential:

1. How does ResolveAI ensure that AI-generated resolutions are not only fast but also contextually fair across diverse use cases (e.g., NFTs vs. DAO governance)? Could there be human-in-the-loop mechanisms for particularly sensitive cases?
2. In what ways do you see ResolveAI strengthening the Metis ecosystem? For instance, could this become a standard arbitration layer for other Hyperion-based apps or DeFi protocols building on Metis?
3. What incentives or protections are in place for community validators to ensure resolution quality and prevent bias or gaming of the RES point system?

Really looking forward to seeing how ResolveAI evolves this could be a game-changer for trust and usability in Web3!

[kirandev]

Hi @priyankg3, thanks for the question! Below is a precise answer on how validators are selected and rewarded in ResolveAI

Validators are selected via a permissioned system requiring a 100 RES token stake to ensure commitment and deter bad actors. Anyone with sufficient RES tokens can stake to become a validator, with disputes randomly assigned to prevent collusion. Validators earn 10 RES per reviewed dispute, plus 20 RES bonuses for high-accuracy reviews matching community consensus. Malicious or inconsistent validations trigger a 10 RES slash.

[kirandev]

hi @han

1. How does ResolveAI ensure AI-generated resolutions are contextually fair across diverse use cases (e.g., NFTs vs. DAO governance)? Could there be human-in-the-loop mechanisms for sensitive cases?

ResolveAI ensures fairness with Alith’s LLM using use-case-specific templates (NFTs: metadata, DAOs: voting data) and weighted scoring (70% on-chain, 20% rules, 10% user input). Sensitive cases (e.g., high-value DeFi, subjective DAOs) escalate to 3+ validators for human-in-the-loop review, logged on-chain. who review Alith’s proposals for accuracy and fairness, with outcomes logged on-chain for transparency.

2. In what ways do you see ResolveAI strengthening the Metis ecosystem? Could it become a standard arbitration layer for Hyperion-based apps or DeFi protocols?

ResolveAI boosts Metis by offering a scalable arbitration layer for Hyperion dApps, resolving DeFi/NFT disputes cross-chain via Metis SDK. It enhances trust and interoperability, serving as a standard for Metis protocols with transparent on-chain logs . boosting trust for Metis-powered dApps.

3. What incentives or protections ensure validator resolution quality and prevent bias or gaming of the RES point system?

Validators stake 100 RES, earn 10 RES per review, 20 RES for accurate consensus matches. Random assignment and 10 RES slashing prevent bias/gaming. Minimum 3 validators per dispute ensures quality.

Enhancement: Add 50 RES staking tier for inclusivity. Implement reputation scores to reduce biased validator weight. Build React dashboard for real-time RES tracking.

[han]

Thanks, this gives a clear picture of how ResolveAI is designed to handle fairness, scalability, and validator accountability. The human-in-the-loop approach for sensitive cases and the transparent on-chain logging are especially reassuring from a user perspective.

Quick question: For dApps looking to integrate ResolveAI, how customizable are the resolution templates or scoring weights per use case?