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DGrid AI released the latest research paper PoQ-Judge, completing the closed loop of decentralized LLM quality assessment with a multi-architecture evaluation framework

The decentralized AI infrastructure network DGrid AI today released its latest research paper "PoQ-Judge," proposing a multi-architecture quality assessment framework that does not require reference answers. This means that in real deployment environments, there are often no standard answers for comparison, yet the protocol can still reliably score the quality of model responses and allocate incentives accordingly. This is a key piece that has long been missing in DGrid's decentralized LLM inference quality assessment system.PoQ (Proof of Quality) is a consensus mechanism independently developed by DGrid, designed to prevent model providers from deploying low-quality models, fabricating data, or hiding computational costs at the protocol level, thereby ensuring service quality and pricing transparency. The DGrid team has been continuously working on PoQ and has published four research papers to date. The newly released PoQ-Judge has trained three assessment models covering different quality and cost scenarios, achieving a correlation of up to 0.747 with human scoring on the retention test set, significantly outperforming all previous reference answer-based evaluators, while reducing assessment costs by over 72% through cascading evaluation and online weight calibration.With the implementation of PoQ-Judge, the entire process from quality assessment → scoring → incentive allocation has completely eliminated reliance on reference answers, thus establishing a closed loop for the quality of decentralized LLM inference.DGrid AI is a decentralized AI intelligent network dedicated to building an open, transparent, and community-driven AI infrastructure. Focusing on model invocation and application experience, DGrid has launched several core products: the AI Gateway that aggregates mainstream large models globally, the one-click deployment platform for AI agents DClaw, the anonymous model competition platform AI Arena, and the intelligent model recommendation assistant Dori, providing one-stop services for developers and users. It is reported that DGrid AI's revenue has surpassed 20 million dollars in six months.

U.S. judge postpones hearing on Aave's application to unfreeze $71 million in stolen ETH

U.S. Judge Margaret M. Garnett in New York postponed the ruling on Aave's emergency application on Wednesday, which aims to unfreeze $71 million in ETH related to the Kelp DAO hacking incident, and requested both parties to submit supplemental briefs before the hearing on June 5. Aave is attempting to reclaim the $71 million in ETH frozen on Arbitrum to assist in the asset recovery efforts from this hacking incident—Kelp DAO suffered losses of up to $293 million from the hack, making it one of the most severe security incidents in the DeFi space this year.However, the U.S. law firm Gerstein Harrow LLP submitted a restraining order to the court in early May, claiming that its client has rights to the aforementioned funds. Aave then filed an emergency motion to lift the freeze, warning that if the funds are not released in a timely manner, it could lead to user liquidations and potentially impact the entire DeFi market. Judge Garnett noted in her ruling that Aave failed to adequately explain how user funds would incur "compound losses" if the restraining order remained in place. She also acknowledged the complexity of the case, the risks faced by the victims, and requested both parties to provide supplemental statements on six key issues, including: whether the hacking transaction is subject to New York state sanctuary principles, the legal distinctions between fraud and theft and what rights the hacker has over the stolen assets, which laws apply to determine the priority of claims for frozen assets, whether constructive trusts are an appropriate remedy, and whether Aave or Arbitrum can identify individual victims and proportionally return assets. Both parties must submit supplemental briefs by May 22.Meanwhile, the overall compensation work for Kelp DAO is progressing. Kelp and Aave announced on Tuesday that the rsETH held by the hacker has been destroyed on Arbitrum, and approximately $278 million in loss tokens will be restored within the next two weeks through the funds of the Aave Recovery Guardian multi-signature wallet. Once the relevant smart contracts are reactivated, all functions of rsETH will return to normal.

Kalshi's ban application was rejected, and a U.S. judge ruled that prediction markets do not take precedence over state gambling regulations

The Chief Judge of the U.S. District Court for the Southern District of Ohio, Sarah D. Morrison, ruled that there is no historical evidence indicating that Congress intended for federal law to take precedence over state regulation of sports gambling, and thus denied the preliminary injunction request filed by the prediction market platform Kalshi.Kalshi had previously sued the Ohio Casino Control Commission in an attempt to prevent it from taking enforcement action against the platform's event contracts under state gambling laws. Last year, the regulatory agency accused Kalshi of operating illegal sports gambling in Ohio.Kalshi argued that the event contracts it offers are derivatives regulated under the Commodity Exchange Act and should fall under the jurisdiction of the CFTC, thereby asserting that federal regulation should take precedence over state gambling laws.However, the judge stated that there is no evidence from historical and legislative context to suggest that Congress intended for the law to supersede state sports gambling regulations, noting that when the Dodd-Frank Act amended relevant laws in 2010, sports gambling was still widely restricted in the U.S.Kalshi announced that it would appeal the ruling. The case is seen as an important test of the legal status of prediction markets, and its outcome could affect the future compliance prospects of other prediction platforms in the U.S., including Polymarket.
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