沃什听证首秀，「新美联储通讯社」：强调高通胀零容忍但未暗示利率路径

marsbitPublished on 2026-07-15Last updated on 2026-07-15

Abstract

美联储主席沃什在国会听证会首秀中，未就未来利率路径给出任何暗示，而是重点强调了对持续高通胀的“零容忍”态度，以及恢复价格稳定是当前首要任务。尽管听证会前公布的6月CPI数据低于预期，但沃什淡化了其重要性，表示不认为抗通胀任务已完成。他重申将依据经济数据来决定利率和资产负债表工具的使用，避免提供前瞻性指引。沃什否认控制通胀与实现充分就业是相互冲突的“残酷选择”，认为价格稳定是经济长期增长的基础。关于资产负债表改革，沃什承诺未来任何调整都将提前与市场充分沟通，以确保政策透明并避免冲击市场。他还多次强调了美联储货币政策的独立性，这一立场获得了部分两党议员的积极回应。总体来看，此次听证会强化了沃什领导下的“数据决定政策”沟通框架，表明未来政策走向将高度依赖经济数据表现，而非预设的利率路径。市场预计美联储将继续坚持价格稳定优先，并通过更透明的沟通来管理预期。

撰文:李丹,华尔街见闻

美联储主席沃什的国会货币政策听证会「首秀」亮出了他的「美联储新政宣言」,没有给市场最关心的降息或加息信号,而是反复强调恢复价格稳定、维护美联储独立性,并承诺未来资产负债表调整将提前充分沟通。

有「新美联储通讯社」之称的记者 Nick Timiraos 认为,沃什当天刻意避免就未来利率路径作出任何暗示,而是将听证会重点放在重申美联储控制通胀的长期目标上。

就在听证会开始前,美国公布的 6 月 CPI 低于市场预期,一度推动市场进一步押注美联储将更快放松政策。不过,沃什淡化了这份数据的重要性,表示不认为通胀任务已完成。

Timiraos 指出,沃什没有借助这份通胀数据释放任何未来政策倾向,也没有透露 7 月议息会议或之后的利率路径,而是坚持强调,美联储拥有利率和资产负债表两项政策工具,未来将依据经济数据决定如何使用这些工具,以实现价格稳定目标。

彭博则认为,这场持续数小时的听证会实际上勾勒出「新美联储」的政策框架:坚持货币政策独立性,坚持 2% 的通胀目标,不接受就业与通胀只能二选一的说法,同时为未来资产负债表改革和央行治理改革留下空间。

受沃什偏鹰派的讲话影响,美债收益率在沃什听证会期间回吐了 CPI 公布后的部分降幅,美元指数收窄约半数 CPI 公布后的跌幅。

五大信号:沃什首次国会听证勾勒「新美联储」政策框架

重申对持续高通胀「零容忍」,断言一次 CPI 回落不足以宣告降通胀胜利

沃什在听证会上强调,美联储对持续高通胀「零容忍」。他说,美联储已经连续多年未能实现 2% 的通胀目标,因此恢复价格稳定仍是当前最重要的政策任务。

面对当天公布、明显低于市场预期的 6 月 CPI 数据,沃什表示:「有人可能会说,(通胀)任务完成(Mission accomplished),我不这么看。」

Timiraos 认为,沃什当天反复重申长期通胀目标,而没有因为单月数据改善调整政策表态,凸显其希望避免市场将一次通胀数据解读为货币政策即将转向的信号。

未透露任何利率路径,强调未来仍将依据数据决定行动

对于市场最关心的下一步利率行动,沃什始终保持克制。

Timiraos 指出,沃什没有暗示未来几次美联储货币政策委员会 FOMC 会议的政策方向,也没有回答市场关于何时可能调整利率的问题,而是强调,美联储拥有利率政策和资产负债表政策两项工具,将根据未来经济数据判断是否以及如何使用这些工具。

不过,沃什同时透露,未来一段时间自己将与 FOMC 同事围绕「是否以及何时需要运用政策工具」展开讨论,并形容届时可能会出现一场「家庭内部激烈讨论(family fight)」。

彭博认为,这番表态意味着,尽管沃什没有给出明确政策指引,他的整体措辞仍偏鹰派,显示其在确认通胀持续回到目标之前,不愿轻易释放宽松信号。

就业与通胀并非二选一的「残酷选择」

针对议员关于美联储双重使命的提问,沃什否认存在所谓「残酷选择(cruel choice)」。

他说,只要价格稳定得以恢复,美国经济便能够持续增长,企业也能够继续扩大招聘,因此控制通胀与实现充分就业并非彼此冲突,而是相互促进。

这一表述进一步强化了其政策理念,即稳定物价本身就是实现长期就业增长和经济繁荣的基础。

资产负债表改革会提前预告,不会突然冲击市场

资产负债表改革一直是沃什上任后的重要改革议题。

不过此次听证会上,他表示,不愿预判正在开展工作的资产负债表改革工作组结论。

与此同时,他承诺,未来如调整资产负债表政策,美联储将提前向市场充分沟通,确保投资者获得充足预期,不会突然采取行动。

沃什再次强调,美联储资产负债表应当服务于货币政策,而非承担财政政策功能。

路透认为,这一表态有助于缓解市场对于新一轮缩表改革可能过快推进的担忧,也意味着未来美联储将更加重视政策沟通和市场预期管理。

坚持货币政策独立性,获得两党部分议员积极回应

面对议员提问,沃什再次强调,美联储将在制定货币政策过程中保持独立,承诺设定利率不会受到政治因素左右。

彭博报道称,尽管沃什在参议院确认过程中几乎未获得民主党支持,但此次听证会上,多位民主党议员仍对其强调央行独立性的立场给予积极评价。

资深国会记者 Steve Dennis 认为,在特朗普持续公开施压美联储降息的背景下,一些民主党议员选择公开支持沃什维护央行独立性的立场,也反映出两党在这一问题上的微妙变化。

沃什听证会强化数据决定政策的沟通框架

市场怎么看沃什听证会?总体来说,沃什的言论并没有改变短期利率前景,但强化了「数据决定政策」的新沟通框架。

Timiraos 认为,沃什此次听证会最大的特点,不是释放新的政策信号,而是刻意没有释放任何关于利率路径的信号。

面对一份低于预期的 CPI 报告,沃什没有顺势讨论下一步是否降息,也没有提供任何前瞻性指引,而是始终围绕恢复价格稳定、美联储独立性以及政策工具展开阐述,延续了其上任以来避免对单次数据或单次会议作出承诺的沟通方式。

彭博则认为,听证会进一步勾勒出沃什领导下美联储的政策基调:继续坚持价格稳定优先,同时推进资产负债表和央行治理改革,并通过更透明的沟通管理市场预期。

对于投资者而言,这意味着未来美联储政策仍将高度依赖数据表现,而不是预设的利率路径,市场也将更加关注沃什未来几个月如何将这一理念落实到 FOMC 的实际决策之中。

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What is AGENT S

Agent S: The Future of Autonomous Interaction in Web3 Introduction In the ever-evolving landscape of Web3 and cryptocurrency, innovations are constantly redefining how individuals interact with digital platforms. One such pioneering project, Agent S, promises to revolutionise human-computer interaction through its open agentic framework. By paving the way for autonomous interactions, Agent S aims to simplify complex tasks, offering transformative applications in artificial intelligence (AI). This detailed exploration will delve into the project's intricacies, its unique features, and the implications for the cryptocurrency domain. What is Agent S? Agent S stands as a groundbreaking open agentic framework, specifically designed to tackle three fundamental challenges in the automation of computer tasks: Acquiring Domain-Specific Knowledge: The framework intelligently learns from various external knowledge sources and internal experiences. This dual approach empowers it to build a rich repository of domain-specific knowledge, enhancing its performance in task execution. Planning Over Long Task Horizons: Agent S employs experience-augmented hierarchical planning, a strategic approach that facilitates efficient breakdown and execution of intricate tasks. This feature significantly enhances its ability to manage multiple subtasks efficiently and effectively. Handling Dynamic, Non-Uniform Interfaces: The project introduces the Agent-Computer Interface (ACI), an innovative solution that enhances the interaction between agents and users. Utilizing Multimodal Large Language Models (MLLMs), Agent S can navigate and manipulate diverse graphical user interfaces seamlessly. Through these pioneering features, Agent S provides a robust framework that addresses the complexities involved in automating human interaction with machines, setting the stage for myriad applications in AI and beyond. Who is the Creator of Agent S? While the concept of Agent S is fundamentally innovative, specific information about its creator remains elusive. The creator is currently unknown, which highlights either the nascent stage of the project or the strategic choice to keep founding members under wraps. Regardless of anonymity, the focus remains on the framework's capabilities and potential. Who are the Investors of Agent S? As Agent S is relatively new in the cryptographic ecosystem, detailed information regarding its investors and financial backers is not explicitly documented. The lack of publicly available insights into the investment foundations or organisations supporting the project raises questions about its funding structure and development roadmap. Understanding the backing is crucial for gauging the project's sustainability and potential market impact. How Does Agent S Work? At the core of Agent S lies cutting-edge technology that enables it to function effectively in diverse settings. Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

829 Total ViewsPublished 2025.01.14Updated 2025.01.14

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