国产开源大模型迎来集群式突破，多款中国自研基础模型面向全球开放走出低成本高效率差异化技术路线 China’s open-source large models achieve collective breakthroughs. Multiple domestically-developed foundation models go global, pioneering an efficient and cost-effective technical path

中国氢能列车单次加氢十分钟续航千里，零下35℃极寒环境稳定运行突破海外技术局限#国产低温氢能技术# China’s hydrogen train runs 1000km after a 10-minute hydrogen refuel, operating stably at -35℃ and breaking foreign technical limits. #DomesticLowTempHydrogenTech#

Coinbase 工程师 Brock Miller 发文称，在 Coinbase 工作八年后，其已于上月离职，并加入 AI 公司 Anthropic，担任技术团队成员（Member of Technical Staff），参与推动 AI 前沿技术发展。Miller 表示，过去 12 个月里，他亲眼看到自己的软件工程工作因 AI 发生巨大变化，并认为这一转变仍处于非常早期阶段。其同时表示，仍然看好 Coinbase 及更广泛的加密行业，未来将继续关注其发展。

原OpenAI 研究员田永龙加入腾讯 OpenAI 前研究员 Yonglong Tian（田永龙）已于近期加入腾讯，知情人士透露，其后续或将参与参与 VLM（视觉语言模型）相关研发。 田永龙此前在 OpenAI 担任 Member of Technical Staff，主要研究方向包括计算机视觉、视觉表征学习（Representation Learning）以及生成式模型。 在 OpenAI 之前，他曾在“谷歌系”深耕多年。2022年底，他加入位于剑桥的 Google Research 担任高级研究科学家，随后于 2024 年 5 月转入 Google DeepMind。

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.