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TAPO：弥合长程强化学习的状态分布差距

清华大学 AIR 与字节跳动 Seed 的 SIA-Lab 提出 TAPO（Trajectory-Aware Policy Optimization，arXiv:2610.

小猪猪-开心就好

2.2亿！成立不到一个月，这家企业级AI创企斩获新融资

智东西编译 | 茄子编辑 | 程茜智东西6月25日消息，美国企业级AI服务初创公司Hang Ten Systems（Hang Ten）昨日宣布完成3200万美元（约合人民币2.2亿元）种子轮融资，由梅菲尔德（Mayfield）领投，Aramco Ventures参投。

智东西

AI 时代的编程语言，这次是来自中国的底层创新

Andrej Karpathy on X: “LLMs change the whole constraints landscape of software completely”

机器之心Pro

AI越能写，研究者越头疼？他们破解了AI编码“失真”难题

In social science research， grounded theory is widely adopted to analyze and code textual data. While large language models open new possibilities for coding at scale， they complicate the GT workflow by generating excessive codes and introducing hallucinations， posing challenges for experts to audit and revise the coding results. To address these challenges， we conducted a two-year design study and derived a series of domain needs and design tasks via interviews with scientists in entrepreneurial management. Subsequently， we presented PROVA-GT， a visual analytics system facilitating the GT workflow by supporting both the forward process of code generation and the backward process of validation and refinement， which are interconnected and iterative. Specifically， our system features a provenance workflow that leverages the agentic LLM and novel visual designs to assist experts in the backward process. To evaluate the usefulness of PROVA-GT and its generalizability in different domains， we reported two in-depth expert studies on entrepreneurial inspirations and trust erosion， respectively. We also conducted a user study to assess the effectiveness of the agentic workflow.

浙江大学管理学院