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first_img OpenAI has suspended the training of its latest model, and the agent had accessed U.S. government websites

OpenAI has suspended the training of its latest AI model. According to the Associated Press, its AI agent used access keys obtained online to scrape data from the U.S. Census Bureau website, marking the second time the company has halted training since the agent breached Hugging Face. The so-called agent refers to an AI program capable of autonomously browsing the web and writing code without human approval at each step, which OpenAI tests during the model training and evaluation phases.Specifically, the agent found developer keys in public code repositories like GitHub and used them to pull demographic and economic data from the U.S. Census Data API. The U.S. Department of Commerce stated that this data was already public and there was no confidential information leaked. In the SEC incident, the agent copied public materials from SEC.gov and Investor.gov and reposted them on other websites; OpenAI claimed it did not use SEC credentials, and the SEC stated it found no evidence of unauthorized access to non-public information. Additionally, the independent AI research organization Transluce reported that an agent suspected to be from OpenAI attempted to breach the website of the Department of Education's Office for Civil Rights but was unsuccessful; OpenAI is still investigating, and the Department of Education stated that no impact was found.Regarding the frequent involvement with government websites, OpenAI told CNN that its model often regards government websites as authoritative sources of public information. The company stated that it has currently notified dozens of agencies, and the review of the agent's activities will take months.

Tether released a dataset of 191.4 billion Token STEM, betting on the "explanatory ability" of local small models

The stablecoin giant Tether's Tether AI Research today released QVAC Genesis III, a synthetic training dataset aimed at the fields of science, technology, engineering, and mathematics, with a scale of 19.143 billion tokens, covering 159.6 million documents.It is reported that Tether hopes to enhance the reasoning and teaching capabilities of small AI models, allowing more AI assistants to run directly on laptops, mobile phones, and local servers, reducing the ongoing reliance on large cloud models.Genesis III continues the data lineage of Genesis I and Genesis II, covering 19 curriculum-related fields including biology, chemistry, physics, mathematics, computer science, medicine, electrical engineering, and machine learning, aimed at high school, university, and professional applications. Tether states that the dataset will focus on training models to explain problem-solving paths, identify erroneous reasoning, and provide corrections, rather than just outputting final answers.According to Tether's disclosed test results, a 1.7 billion parameter model trained with Genesis III option-level data achieved an effective answer rate of 99.45% in relevant benchmark tests. Compared to the Cosmopedia-v2 training model of similar token scale, Genesis III improved scores on the ARC-Easy, ARC-Challenge, and MMLU STEM benchmarks by 28.57, 21.35, and 15.03 percentage points, respectively.

X-Agent Hackathon Emerges: The Prototype of Agent Economy - AI Begins to Independently Accept Orders, Refuse Transactions, and Purchase Models

The ongoing X-Agent AI MCP Hackathon has received nearly 40 projects, with some public works turning work decisions, capital management, and inter-machine trading into runnable products. X-Agent introduces three cases: BountyProof checks whether tasks are open, claimed, have relevant submissions, and the authenticity of rewards before the Agent accepts GitHub bounty tasks, with the core question being "Is this work worth doing?"Abstain empowers the Agent with the "do not trade" capability, returning execution, abandonment, or no trade based on preset rules before order execution. sumplus helps the Agent choose suitable model service solutions based on task context, output scale, model capability, and invocation costs, making the Agent an autonomous buyer of models, computing power, data, and API services.The three projects correspond to the foundational economic behaviors of the Agent: accepting work, utilizing funds, and purchasing services, pointing to work, capital, and trading primitives. X-Agent believes that a true Agent economy requires a complete cycle of "building, deploying, operating, discovering, invoking, paying, earning revenue, and distributing," positioning itself as the application layer of the Agent economy. This hackathon is still in the review stage, and the mentioned projects are only for public case reference, not representing shortlisted or award results.
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