日本股市周一市值增加约5000亿美元，日经225指数上涨5.0%，东证指数（TOPIX）上涨4.2%。此次上涨主要受两大因素推动：一是中东局势缓和，市场对油价和通胀的担忧下降，而日本作为全球最大的石油进口国之一因此受益；二是AI和半导体板块热度再度升温，吸引大量资金回流。 涨幅最大的公司包括： 铠侠控股（285A TYO）+11.96% 乐天集团（4755 TYO）+10.31% 爱德万测试（6857 TYO）+7.67% 东京电子（8035 TYO）+7.00% 三菱重工（7011 TYO）+3.36% 丰田汽车（7203 TYO）+4.58% 日立制作所（6501 TYO）+1.72% 半导体、电子和工业股领涨大盘，投资者逢低买入，并重新涌入日本AI产业链相关股票。

Prompt 8：生成完整产品大纲 “我想为 [audience] 做一个关于 [topic] 的 [product type]。帮我生成完整大纲，包括：产品标题、核心承诺、模块结构、每一部分内容、附加赠品、快速见效设计，以及最终交付形式。要求简单实用、值得付费，避免学术化。”

🚨爆料：DeepSeek Harness 将于今天开启正式公测🔥 刚刚一张据称说DSH 官方内测群的截图在流传 内容是： 0813 计划发布 DSH 公测版，今晚将推送 DeepSeek Harness 最后一个内测版本，让开发者完成最后的插件兼容；插件仓库需要带上 #dsh# topic。 也就是今天，DeepSeek Harness 将正式结束封闭内测，开始对外开放。 同时，内测期间开发的插件也可以迁移到开发者自己的账号下，并正式公开。 看起来 DeepSeek 这次不只是发一个 Harness，连围绕 Harness 的插件生态也准备一起放出来了。 今天可以蹲一下 DeepSeek Harness🫡

如果你在用 LLM 做评审，这篇值得看。（建议收藏） 这是目前很实用的一种评估方法。 传统整体评分会隐藏模型的判断过程，也容易出现评分上限的问题。 BINEVAL 的做法是：把每个评估标准拆成多个简单的是/否问题，分别判断，然后再汇总成多维评分。 每个问题的判断都能单独查看，所以可以清楚知道哪里做得不好；这些结果还能直接用来优化提示词。 在 SummEval、Topical-Chat、QAGS 上，它不需要训练，效果和 UniEval、G-Eval 相当甚至更好，尤其在事实准确性上更强。 论文：

机器人圈也被 AI 的 scaling 卷麻了 截图是 ICRA 2026 一个数据统计，感觉蛮有意思的，分享一下。比如论文关键词的分布，中美加起来占一半以上的江山。 投稿 4947 篇，接收 1882 篇，接收率 38.04%。2021 年时候投稿量大概 4000 篇左右，机器人圈也在被 AI 的 scaling 卷麻。 Hot topics 是 Manipulation、Planning、Mapping/Perception 3D，SLAM/Localization，Object Detection/ Tracking。 author keyword top 是：Deep Learning for Visual Perception、Reinforcement Learning、Motion and Path Planning、Imitation Learning。 btw，这个数据不是 ICRA 官方做的，是韩国 DGIST 一个助理教授 Giseop Kim 做的，现在vibecoding一个东西变得无比容易。just do it… 想起来去年还写过一场 ICRA 2025 的 keynote 辩论，当时议题是“Data will solve robotics: True or false？” 转眼一年过去了……大家觉得这一年机器人领域的进展快吗？现在关于Data will solve robotics的争论，大家觉得有答案了吗🐶

和Quant Alex @StochAlex07 讨论： SABR Theta与Spot Theta+Vol Theta+Cross Theta的异同与应用，以及SABR模型自洽性分析。 **English Summary of the Chat** **SABR Theta vs Spot Theta + Vol Theta + Cross Theta** The conversation between **Alex Wu** (white bubbles) and **Jeff Liang** (green bubbles) is a technical discussion focused on **SABR Theta versus Total Theta** (i.e., Spot Theta + Cross Theta + Vol Theta), model self-consistency, PDE residual, and the correct definition of SABR Greeks. ### Key Points Discussed: 1. **SABR Gamma = Spot Gamma** (first major question, raised by Jeff) Jeff asked whether SABR Gamma (\(\partial^2 P / \partial F^2\)) is identical to Spot Gamma and whether it includes the dependence of \(\sigma_B\) on \(F\). He also provided the full chain-rule expansion of SABR Gamma in terms of Black-76 Greeks. Alex confirmed the understanding and **later affirmed in code** that this is exactly how SABR Gamma is implemented in their system. 2. **SABR Theta vs Total Theta and Model Self-Consistency** (main topic, led by Jeff) Jeff shared a clear 3-point understanding: - SABR Theta is computed directly via the SABR approximation formula to obtain \(\sigma_B\), then applying the Black-76 chain rule: \(\partial P/\partial t =\) BS_Theta(\(\sigma_B\)) + BS_Vega \(\cdot \partial\sigma_B/\partial t\). - Total Theta is the exact decomposition from the SABR PDE (Spot Theta + Cross Theta + Vol Theta). - When the model is **fully self-consistent** (Residual = \(\partial P/\partial t + \mathcal{L}P = 0\)), SABR Theta = Total Theta; otherwise the difference is the unexplained PnL caused by the approximation error in the Hagan formula (especially pronounced in long-dated, high vol-of-vol, or high-skew options). 3. **Practical Implication – Theta Decomposition Decision** (comment by Alex) Alex noted that whether to perform Theta decomposition depends on the risk-management approach: - Without decomposition → use SABR Gamma vs. dP/dt. - With decomposition → SABR Gamma maps to Spot Theta, Vanna to Cross Theta, and Volga to Vol Theta. **Overall Tone**: The discussion is highly technical and collaborative. Jeff drives the conversation by asking clarifying questions and presenting a well-structured 3-point summary of his recent study. Alex provides confirmations, practical insights, and code-level validation. Both participants demonstrate a strong command of SABR model nuances, particularly the relationship between approximation error, PDE residual, and real-world risk management.

这个话题大家平时都不想提，但目前人还不能永生。每个平台都应该有个“遗嘱功能”：不在的时候，自己的资产按照指定的比例分到几个指定的账号。法律监管条款里也应该允许未成年的小孩也能有账号（可以不允许交易，但可以收款）。 This is a topic people avoid, but the fact is, humans cannot live forever yet. Every platform should have a ‘will function’—so that when someone is no longer around, their assets can be distributed to designated accounts according to specified proportions. Regulatory frameworks should also allow minors to have accounts (they can be restricted from trading, but should be allowed to receive funds).”