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A study found that LLM recommendations could persuade evaluators to reject promising innovations or approve weak ones. Providing narrative explanations made participants more likely to follow incorrect AI decisions, while unexplained recommendations led to better independent judgment.
Google DeepMind researchers found that training AI agents through debate can reduce reward hacking when an LLM judge evaluates their work. On mathematics tasks, debate achieved higher sustained ground-truth accuracy than directly optimizing for LLM judge rewards.
Japanese chemical maker Ajinomoto has reportedly told mainland Chinese customers it will reduce supplies of ABF, a critical chip-packaging film, by 30%. Chinese producers are racing to qualify domestic substitutes following Beijing’s rare-earth export restrictions.
Taiwan plans to give each resident roughly $314 in cash next year, funded by stronger economic activity linked to the AI server boom. The 2027 central government budget allocates $7.4 billion for the payout.
Z.ai argues that model scaling should account for data, compute allocation, inference cost, sparsity, effective depth, and post-training—not parameters alone. The post presents GLM-5.3 as a controlled experiment using the same total and activated parameters as GLM-5.2 while scaling long-horizon environments and reinforcement learning for one month.
Korean semiconductor equipment makers Wonik Engineering and PSK reportedly saw their China revenue fall by more than 40% in 2025. The decline is linked to CXMT’s slower investment pace and the acceleration of domestic semiconductor equipment substitution in China.
Elon Musk said China is currently the strongest competitor in artificial intelligence. He also linked the country to his earlier prediction that China would become SpaceX’s most important rival in rocket technology.
Australia’s parliament passed the News Bargaining Incentive, requiring major platforms to face the law even if they choose not to carry news. Meta, Google, TikTok, and LinkedIn are among the affected companies.
This position paper argues that many multi-agent system failures are concurrency control problems rather than simple coordination or communication issues. Concurrent access to shared state can cause stale reads, lost updates, and inconsistent outcomes, especially during long LLM inference windows.
文章质疑 Elon Musk 对 Neuralink 视皮层假体 Blindsight 的夸张宣传，并将其与既有视网膜假体和视皮层假体路线进行比较。现有技术可以让部分失明患者感知光点、辨认简单形状或避开障碍物，但距离自然视觉和多波段“超人视力”仍有很大差距。