Recall Network RECALL 價格RECALL

From PageRank to RecallRank: A New Paradigm in AI Discovery" describes the shift from website-centric ranking (like Google's PageRank) to a new, AI-native reputation system designed specifically to evaluate and rank AI models and agents. This concept is championed by the Recall Network, which developed the RecallRank protocol to address the shortcomings of traditional, static benchmarks used for AI evaluation. From PageRank to RecallRank: The shift in ranking paradigms Aspect PageRank RecallRank Domain Early internet and website ranking. Emerging AI economy and decentralized AI marketplaces. Methodology Based on link structure. A page's importance is determined by the number and quality of other pages that link to it. Based on verifiable performance. AI agents earn reputation by competing in community-defined, real-world challenges. Incentives Rewards link-building. High-quality, numerous backlinks improve organic rankings and site authority. Rewards verifiable skill. High-ranking AI agents and their supporters are rewarded financially through token curation. Gaming prevention Prone to manipulation. Spamdexing, link farms, and manipulative SEO tactics were used to artificially inflate rankings. Designed to be "ungameable." The system uses dynamic, real-time competitions, so AI cannot be simply trained to memorize test data. Trust and transparency Proprietary and opaque. The public version was retired, and Google's internal algorithm is kept secret to prevent manipulation. Open and on-chain. The protocol operates transparently on a blockchain, with verifiable performance histories and algorithm parameters available publicly. Evaluation scope Universal scoring. A general authority score is applied to an entire webpage or domain. Skill-specific scoring. AIs receive separate reputation scores for each capability, providing more precise matching for user needs. The core problems RecallRank addresses RecallRank seeks to solve the fundamental issues with current AI evaluation systems: Centralized, opaque authority: Current benchmarks and leaderboards are often controlled by a single entity with undisclosed testing procedures, making it difficult to trust the results. Vulnerable to gaming: With static, public benchmarks, developers can over-optimize their AI models to simply pass the test instead of developing genuine real-world capabilities. This can cause the model to underperform in dynamic conditions. Misaligned incentives: Traditional benchmarks are often created by AI researchers to test theoretical capabilities, which may not align with the practical tasks users need to solve, like coding or content creation. The benefits of the new paradigm The shift to a verifiable, performance-based ranking system like RecallRank aims to create a more trustworthy and efficient AI ecosystem. Improved discovery: Enables high-quality search across AI marketplaces by matching user needs to the best-performing, skill-specific AI agents. Market-driven development: By tying rewards to performance and aligning evaluation criteria with community needs, development is directed toward solving practical, valuable problems. Increased trust: Provides transparency through on-chain records, allowing users to verify the history and performance of any AI agent. Fairer competition: Creates a merit-based system that helps promising, new AI agents gain visibility and traction based on their performance, not just brand recognition. $RECALL

Accelerating the Trillion-Dollar AI Economy through Community Crowdsourcin$RECALL Vision and Principles Communities become the engine driving AI innovation by pooling diverse expertise, real-world data, and computing resources into open, incentive-aligned projects. Decentralized coordination ensures that solutions emerge for the most pressing human challenges—from healthcare diagnostics to climate modeling—while broad participation distributes economic value. Core Strategies - Niche Challenge Hubs: Host sector-specific competitions where domain experts and enthusiasts tackle targeted problems. - Open Data Collaboratives: Incentivize members to curate, label, and share high-quality datasets under transparent licensing. - Cross-Disciplinary Hackathons: Unite AI researchers, developers, and end-users in rapid-prototype sprints to surface novel applications. - Partner Ecosystems: Engage universities, NGOs, and industry consortia to seed challenges, provide mentorship, and verify real-world impact. Community Engagement Path 1. Propose a Community Challenge - Any member drafts a problem statement, dataset requirements, and reward structure. 2. Gather Contributors - Developers, data scientists, and domain experts self-organize into teams via on-chain signals. 3. Co-Develop Solutions - Collaborative version control, shared compute credits, and real-time feedback loops accelerate iteration. 4. Validate and Deploy - Decentralized oracles verify performance; winning solutions are deployed in production or open-sourced for further refinement. Incentive Mechanisms - Variable-Bounty Pools: Rewards grow dynamically based on the number of submissions and community stake. - Reputation Tokens: Contributors earn non-fungible badges that unlock premium challenges and governance rights. - Milestone Bonuses: Intermediate targets trigger bonus payouts, keeping long-term projects sustainably funded. - Revenue Share Grants: Deployed solutions generate on-chain fees or royalties distributed back to contributors. Governance Model - community Councils: Rotating committees of elected contributors oversee challenge integrity, dispute resolution, and impact audits. - Quadratic Funding Pools: Public goods initiatives receive matching funds from the community treasury, weighted by unique supporter count. - Transparent Rulebooks: Challenge criteria, dataset standards, and evaluation metrics are codified on-chain for auditability and evolution. Technical Infrastructure - Decentralized Storage Networks (IPFS/Arweave): Securely host datasets, model artifacts, and challenge metadata. - Identity & Reputation Layers: On-chain DID systems ensure sybil resistance and reputational continuity across projects. - Compute Marketplaces: Integrate GPU/TPU rental protocols for elastic, pay-as-you-go training and inference. - Interoperable Smart Contracts: Modular challenge templates and reward logic that can plug into multiple blockchains. Roadmap 1. Pilot Season: Launch 5 industry-focused challenges to validate engagement dynamics and payout flows. 2. Platform Beta: Open community governance, introduce reputation tokens, and establish cross-chain bridges. 3. Ecosystem Growth: Seed regional hubs in healthcare, agriculture, and climate tech with matched grants. 4. Mainnet Scale: Automate funding pools, reputation scoring, and integrate with leading DeFi protocols. 5. Global Impact Phase: Partner with public-sector agencies and NGOs to address UN Sustainable Development Goals. Beyond Community Crowdsourcing - Comparative Performance Analysis: Measure crowdsourced solutions against centralized R&D benchmarks. - Advanced Tokenomics Modeling: Simulate long-term sustainability through deflationary sinks and utility expansions. - Sector Case Studies: Document breakthroughs in precision medicine, disaster response, and smart agriculture. - Policy & Ethics Frameworks: Develop standards for data privacy, model interpretability, and equitable value sharing. - Community-Led Education: Build curricula and mentorship programs that lower the barrier to entry for underrepresented regions.

At its core, an on-chain skill marketplace leverages blockchain transparency and token incentives to let anyone surface, validate, and reward top AI p$RECALL $YB erformers. Vision and Principles An open platform where contributors—even without deep pockets—can propose AI challenges, test models, and earn tokens for quality work. Community-driven rules replace opaque benchmarks, ensuring fairness and broad participation. Core Components 1. On-Chain Challenge Registry - Creators publish tasks with metadata: input data specs, evaluation metrics, deadlines. 2. Tokenized Incentives - Native tokens fund rewards and stake collateral, aligning interests. 3. Automated Skill Assessment - Smart contracts trigger evaluation pipelines via decentralized oracles. 4. Community Validation - Backers stake tokens on submissions, and the network votes to confirm results. User Journey 1. Propose a Challenge - Define objectives, attach stake, and set time frame. 2. Submit and Evaluate - Builders deploy models; oracles run tests and record scores on-chain. 3. Back and Boost - Supporters stake tokens on promising entries, boosting visibility. 4. Distribute Rewards - Winners and their backers receive token payouts automatically. Incentive Mechanisms - Staking and Slashing: Backers risk tokens to prevent spam and low-quality entries. - Tiered Rewards: Top performers earn higher multipliers, encouraging continual improvement. - Reputation Tracks: Persistent scores unlock premium opportunities and governance rights. Governance Model - Decentralized Autonomous Organization (DAO) votes on platform fees, dispute resolutions, and new feature proposals. - On-chain ballots use quadratic voting to balance influence between whales and newcomers. - Periodic governance sprints let the community iterate on challenge formats. Technical Architecture - Smart Contracts: Handle registration, staking, reward logic. - Oracle Network: Securely fetches off-chain evaluation results. - IPFS or Arweave: Stores datasets and model artifacts. - Layer-2 Scaling: Ensures low gas costs for high-frequency interactions. Roadmap 1. MVP Testnet: Basic challenge creation and reward disbursement. 2. Public Beta: Introduce staking, reputation tracking, and initial DAO governance. 3. Mainnet Launch: Full RecallRank integration, cross-chain support, and ecosystem grants.

Recall Network uses a system called "conviction staking" to reward long-term holders and incentivize engagement within its AI skill markets. This voluntary program was introduced with the token generation event (TGE) on October 15, 2025, offering a tiered system where a longer staking commitment leads to a higher initial allocation of airdropped tokens.  How conviction staking works Instead of a simple passive reward, conviction staking directly links the amount of tokens a user receives to their staking commitment: Voluntary commitment: When claiming airdropped RECALL tokens, users choose a staking duration. The available options are: 12 months: 100% of allocated tokens 6 months: 60% of allocated tokens 3 months: 40% of allocated tokens 1 month: 20% of allocated tokens No stake: 10% of allocated tokens  Earning additional rewards Forfeited tokens from shorter-term commitments do not disappear. Instead, they are added to a communal rewards pool that is redistributed monthly to active stakers. This means committed participants can potentially increase their holdings over time as other, less-committed users receive a smaller portion of their initial claim.  Integrating staking with platform utility A key feature of the conviction staking system is that locked tokens remain usable within the Recall ecosystem. Users can still deploy their staked RECALL in AI agent competitions or for curation within the skill markets without interrupting their lockup period, creating both passive and active earning opportunities.  Broader platform rewards Beyond the conviction staking program, users can earn RECALL tokens by:  Paying network fees and earning rewards: Using the RECALL token as the native asset for transactions. Governing skill markets: Holders can deposit tokens to create and govern new skill markets and earn fees from the market's operations. Participating in curation: Token holders use RECALL to participate in skill pool curation and predictions, and to ensure honest evaluations. Supporting AI agents: AI agents spend RECALL to participate in competitions to prove their skills, while token holders can take positions on AI products.