
Chromia's EVAL by Virtuals current market price is $0.00028048 with a 24 hour trading volume of $353. The total available supply of Chromia's EVAL by Virtuals is 1.00B EVAL with a maximum supply of 1.00B EVAL. It has secured Rank 4388 in the cryptocurrency market with a marketcap of $280.61K. The EVAL price is 0.34% up in the last one hour.
The high price of the Chromia's EVAL by Virtuals is $0.00028229 and low price is $0.00026018 in the last 24 hours. Live prices from all markets and coin market Capitalization. Stay up to date with the latest price movements. Check our coin stats data and see when there is an opportunity to buy or sell at best price in the market.
4388
$0.00028048
$280.61K 6.52%
$280.61K
$353
1.00B EVAL
1.00B EVAL
1.00B EVAL
$0.00028229
$0.00026018
$0.0166 98.31%
28 Jan 2025
$0.00025663 9.29%
15 Oct 2026
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0.34%
6.47%
2.21%
17.12%
50.74%
7.69%
22.35%
66.42%
No historical data available for .
Compare live prices of Chromia's EVAL by Virtuals on top exchanges.
| # | Exchange | Pair | Price | Volume(24h) | Trust Score |
|---|---|---|---|---|---|
| Uniswap V2 (Base) | 0XDD78523217390BB0D49C7601E7E54C36D71622F0/0X0B3E328455C4059EEB9E3F84B5543F74E24E7E1B | $0.00028048 | $353 |
We present EVAL Engine (Evaluation Validation Architecture), a decentralized framework for evaluating AI agents with a focus on crypto-native agents through verifiable real-time assessments and continuous learning capabilities.Our system utilizes Chromias gas-free relational blockchain architecture to enable transparent, immutable, and cost-effective evaluation of AI agent performance. The system incorporates multiple LLM-as-a-judge[1] and social engagement metrics for continuous reinforcement learning via feedback loop and reward system.We demonstrate EVAL Engine can achieve efficient, secure evaluations while adapting to evolving performance standards through engagement-driven feedback loops.We also present a comprehensive roadmap for the development of EVAL Engine, including API development, data preparation, model development, and model deployment.

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