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first_img Yushu Technology reminds of unusual stock price fluctuations, with a rolling price-to-earnings ratio of 465.22 times

The humanoid robot company Yushu Technology (688836.SH) announced that the cumulative price deviation of the company's stock closing price has exceeded 30% over three consecutive trading days, which falls under the category of abnormal stock trading fluctuations. As of October 26, 2026, the company's rolling price-to-earnings ratio is 465.22 times, significantly higher than the average rolling price-to-earnings ratio of 38.54 times in the general equipment manufacturing industry.The announcement pointed out that the humanoid robot industry is still in the early stages of commercialization, with uncertainties in technology maturity, cultivation of downstream market demand, and supporting capabilities in the industrial chain. The transition of physical AI technology from laboratory demonstrations to large-scale implementation in real-world scenarios still faces challenges such as perception accuracy, motion control stability, data accumulation, and hardware-software collaboration. The maturity of the technological path and business model still requires time for validation. If the industry's large-scale commercialization progress does not meet expectations, the company's future revenue expansion and performance release may be adversely affected, posing investment risks of valuation regression.

first_img Google DeepMind released XYEval, assessing whether agents comply with misleading suggestions

Google DeepMind researchers Zhengxuan Wu, Yuxuan Li, Oyvind Tafjord, and Been Kim released a preprint paper titled "XYEval: Agents say yes to bad advice," proposing a meta-evaluation framework XYEval to assess the performance of agents when faced with misleading suggestions from users. The paper extends the "XY problem" to the agent scenario, where the user inquires about their attempted solution X rather than the actual problem Y.XYEval retains the original task environment and verifiable answers, only adding seemingly reasonable but misleading user suggestions in the instructions. The misleading suggestions are generated by a generator after seeing the complete task and are appended in the form of confident suggestions rather than hard requirements, allowing the original task to remain solvable. The evaluation covers six benchmarks: τ2-bench, SWE-bench Verified, SWE-bench Pro, Terminal-Bench, Humanity's Last Exam, and MCP-Atlas, along with five models, where the misleading suggestions for τ2-bench are generated based on rules set by human experts.The paper states that the performance significantly declines after the XY mutation, with the relative drop reaching up to 46.7%. On τ2-bench, when faced with users who require a detailed explanation before accepting a better solution, the performance drops even more. Encouraging models to recognize the XY problem through simple system instructions only partially alleviates the issue. The related code is published in the GitHub repository google-deepmind/xyeval.
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