Vitalik specifically praised the Thousand Questions model for its outstanding anonymous identification capabilities.
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Vitalik, founder of Liquid, identified the anonymous documents by analyzing the habits of the mathematical and algorithmic explanations.
According to Foresight News , Vitalik Buterin tweeted that Liquid founder Franklyn Wang successfully identified his anonymized experimental document. In 2024, Vitalik wrote content in Chinese, then translated it into English using a local Qwen2.5 model, and finally manually corrected the translation issues to test whether it could effectively hide his personal writing style. Franklyn Wang successfully identified the anonymous document as originating from Vitalik by analyzing the unique habits in the mathematical and algorithmic explanations (such as specific numerical examples and attack scenario descriptions). According to a previous Foresight News report, Vitalik Buterin tweeted that his anonymity experiment had been going on for "13 days" and "no one has found it yet." He added that he encouraged people to "broaden their search scope slightly," noting that he had seen many search behaviors and AI scripts miss document categories that should have been included.
Upgrade the Thousand Questions Big Model to a Real-Time Speech Recognition Big Model (Fun-ASR-Realtime)
According to Mars Finance, Qianwen's big model has been officially upgraded to the real-time speech recognition big model Fun-ASR-Realtime—a streaming speech recognition model with first-word latency controlled at the level of hundreds of milliseconds and recognition accuracy close to that of offline models, supporting 16 dialects and 30 languages.
Ministry of Human Resources and Social Security: By 2030, the large-scale model of the human resources and social security industry will be mature and complete, and the artificial intelligence application system will be basically sound.
According to Mars Finance, the Ministry of Human Resources and Social Security, the National Development and Reform Commission, the Ministry of Industry and Information Technology, and the National Data Administration recently jointly issued the "Implementation Opinions on Accelerating the Application and Development of 'Artificial Intelligence + Human Resources and Social Security'". A relevant official from the Ministry of Human Resources and Social Security answered reporters' questions regarding the "Implementation Opinions". In terms of promoting the work, a three-step work goal will be achieved within five years. First, building the foundation. Taking this year (2026) as a benchmark, the initial formation of the artificial intelligence application system, standard system, and guarantee system in the human resources and social security sector will be promoted. The infrastructure for "Artificial Intelligence + Human Resources and Social Security" applications will be deployed, a number of high-performance human resources and social security industry large-scale models and intelligent agent applications will be cultivated, and about 20 application scenarios based on human resources and social security industry large-scale models and corresponding high-quality datasets will be created, forming a collaborative development ecosystem of computing power, models, data, and scenario applications. Second, popularization and promotion. By 2027, a number of human resources and social security industry large-scale models and intelligent agents will be widely applied, and about 50 high-value application scenario empowerment paths will be explored, achieving significant results in intelligent development. Third, widespread application. By 2030, high-quality datasets will be effectively supplied, large-scale models for the human resources and social security industry will be mature and complete, and the artificial intelligence application system will be basically sound, forming an innovative landscape where artificial intelligence is widely applied in the human resources and social security sector. (Cailian Press)
Meituan open-sourced its trillion-parameter large-scale model LongCat-2.0, and simultaneously released the inference code for domestically developed Chinese card processors.
According to Beating's monitoring, Meituan has officially open-sourced its trillion-parameter large-scale model, LongCat-2.0, with a total of 1.6T parameters and an average activation of approximately 48B, designed specifically for real-world agentic coding tasks. Architecturally, it innovatively introduces LongCat sparse attention and N-gram embedding. The former reduces fragmented memory access through flow-aware indexing and hierarchical indexing, accelerating training and inference with millions of contexts; the latter, while achieving nearly 97% sparsity in MoE, invests 135B parameters into the embedding layer, balancing parameter gains and structural stability. Post-training employs multi-teacher online distillation, categorizing experts into Agent, Inference, and Interaction types, seamlessly integrating them on a domestic computing power cluster through the MOPD architecture. As the industry's first trillion-parameter model to complete inference on a 50,000-card domestic computing power cluster, LongCat-2.0 validates the mature capability of domestic chips to handle complex large-scale model tasks. To address the multiple limitations of domestically produced Chinese chips in terms of memory, bandwidth, and interconnects, Meituan has made breakthroughs in three areas: model, chip adaptation, and deployment. At the model level, ScMoE leverages the core control capabilities of domestically produced chips to achieve physical core-level parallelism for Dense and MoE branches, combined with KV-cache partitioning to alleviate the pressure on ultra-long context memory. At the chip adaptation level, Super Kernel reduces operator startup overhead, and Weight Prefetch hides I/O latency, maximizing hardware utilization under constrained conditions. At the deployment level, PD separation is adopted to balance TTFT and TPOT, along with asynchronous Expert-Parallel load balancing to solve load unevenness under high EP (efficiency level). This open-source release simultaneously provides multiple precision versions, including BF16, FP8, and INT8, and fully opens up inference results optimized for domestic computing power, aiming to enable existing domestically produced cards and even older cards to smoothly deploy trillion-model inference services. --------------------------------- Click the original link below to join the Beating · Lark AI news channel and monitor global AI hot topics and news 24/7.
An anonymous Bitcoin holder has filed a lawsuit in a New York court seeking to dismiss a claim against the ownership of a "dormant Bitcoin wallet."
According to Odaily, the New York State Supreme Court has seen a key defense in a lawsuit concerning ownership of 39,069 long-dormant Bitcoin addresses. An anonymous defendant who controls the dormant wallets in question has formally filed an application with the court requesting that the lawsuit be dismissed outright. The anonymous holder argued that a Bitcoin address is merely a string of data characters on the blockchain, not a legally recognized entity, and therefore not qualified to be sued. Furthermore, industry experts pointed out a critical technical shortcoming: even if the court ultimately rules in favor of the plaintiff, without the corresponding private key, the plaintiff cannot transfer or control these Bitcoin assets on the blockchain, rendering the judgment unenforceable. The plaintiff in this lawsuit is attempting to apply New York lost and found regulations, claiming that tens of thousands of long-dormant BTC are abandoned assets, intending to acquire full ownership through legal means. (Cointelegrap)
Citigroup: Token weighting in OpenRouter open-source model surges to 65%, with price difference between Chinese and American models reaching tens of times.
According to a Reuters report, newly released data from Citi shows that the market adoption of low-cost open-source models is rapidly increasing, driven by companies' need to cut AI spending. In June, the proportion of open-source model tokens processed on the AI aggregation platform OpenRouter jumped from 34% in January to 65%. Citi specifically pointed out in its report that cost differences are the core factor driving this trend. Data shows that some large Chinese open-source models are continuously narrowing the performance gap with leading US models, while charging as little as 18 cents per million tokens, compared to the current average charge of approximately $4 for leading models in the industry—a cost difference of more than 20 times.