NEW YORK, July 2, 2026 /PRNewswire/ — Lazard, Inc. (NYSE: LAZ) will announce its second quarter and first half 2026 financial results in a press release to be issued Thursday morning, July 23, 2026. The press release will be available in the News & Announcements section of Lazard’s website at www.lazard.com.

Lazard will host a conference call at 8:00 a.m. ET on July 23, 2026, to discuss the company’s financial results. The conference call can be accessed via a live audio webcast available through the Investor Relations section of Lazard’s website at www.lazard.com, or by dialing 1 800-445-7795 (toll-free within the U.S. and Canada) or +1 785-424-1699 (outside of the U.S. and Canada) 15 minutes prior to the start of the call and using the conference ID: LAZQ226.

A replay of the conference call will be available by 10:00 a.m. ET on July 23, 2026, through the Investor Relations section of Lazard’s website at www.lazard.com, or by dialing 1 800-839-2382 (toll-free within the U.S. and Canada) or +1 402-220-7201 (outside of the U.S. and Canada).

About Lazard

Founded in 1848, Lazard is the preeminent financial advisory and asset management firm, with operations in North and South America, Europe, the Middle East, Asia, and Australia. Lazard provides advice on mergers and acquisitions, capital markets and capital solutions, restructuring and liability management, geopolitics, and other strategic matters, as well as asset management and investment solutions to institutions, corporations, governments, partnerships, family offices, and high net worth individuals. Lazard is listed on the New York Stock Exchange as Lazard, Inc. under the ticker LAZ. For more information, please visit Lazard.com and follow Lazard on LinkedIn.

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SOURCE Lazard

GUANGZHOU, China, July 2, 2026 /PRNewswire/ — POMDOCTOR LIMITED (the “Company”) (NASDAQ: POM), a leading online medical services platform for chronic diseases in China, today announced a strategic upgrade, officially positioning the Company as a foundational infrastructure provider for predictive healthcare data and services.

The strategic upgrade reflects the Company’s continued evolution from a traditional online healthcare platform toward an artificial intelligence (“AI”)-enabled healthcare infrastructure provider. By integrating wearable technologies, AI, physician services, real-world healthcare data, and healthcare payment networks, POM aims to develop an intelligent healthcare ecosystem that enables predictive, continuous and personalized healthcare. The Company believes this integrated platform will support the healthcare industry’s broader transition from reactive treatment to proactive health prediction, prevention, and long-term management.

A New Strategic Positioning

Traditionally, online healthcare platforms have primarily focused on one-time consultations, fragmented healthcare data and reactive medical services delivered after symptoms appear. The business models are generally centered on consultation services and pharmaceutical fulfillment, with limited capabilities for continuous health management.

POM’s strategic upgrade reflects the Company’s ambition to move beyond the traditional online healthcare mode. Instead of providing episodic healthcare services, POM is building a predictive healthcare data and services infrastructure that supports the entire healthcare journey, from prevention and early risk identification to diagnosis, treatment, rehabilitation and long-term chronic disease management.

Unlike traditional platforms that rely on fragmented clinical data, POM is working to establish a continuous, dynamic and multi-dimensional healthcare data ecosystem powered by wearable technologies and real-world healthcare data. Aligned with this ecosystem, a diversified revenue business model is under development, supported by AI-powered services, healthcare data, professional medical services and ecosystem partnerships, rather than depending primarily on pharmaceutical sales.

By integrating real-time data, AI Analytics, physician intervention and healthcare payment networks, this closed-loop healthcare ecosystem enables more proactive, personalized and data-driven healthcare delivery.

Three Trends Accelerating Predictive Healthcare

POM believes the healthcare industry is approaching a significant inflection point as three long-term structural trends converge.

Growing Chronic Disease Burden

Chronic diseases continue to account for the majority of global healthcare expenditures and mortality. As populations age and healthcare costs continue to rise, traditional reactive healthcare models are becoming increasingly unsustainable, creating growing demand for continuous monitoring, early intervention, and long-term health management.

Technological Paradigm Shift

Rapid advances in artificial intelligence, wearable sensors and remote patient monitoring (“RPM”) technologies are making continuous health monitoring commercially viable. These technologies enable the collection and analysis of real-time physiological data beyond traditional clinical settings, providing the technological foundation for predictive healthcare.

Healthcare Payment System Transformation

Healthcare reimbursement models are increasingly shifting from paying for treatment toward supporting prevention, chronic disease management, and long-term health outcomes. The expanding coverage of RPM reimbursement policies is strengthening the commercial foundation for predictive healthcare while creating stronger alignment among patients, healthcare providers, and insurers.

Together, these structural trends are reshaping healthcare and creating a compelling opportunity for predictive healthcare to become the next major evolution of digital medicine.

POM’s Healthcare Intelligence Flywheel

To execute its strategic vision, POM has established a healthcare intelligence flywheel designed to continuously improve healthcare outcomes through strengthening the Company’s AI capabilities, healthcare data assets and ecosystem value.

The flywheel begins with wearable devices, which continuously collect real-world physiological data and establish personalized health profiles, creating the foundation for predictive healthcare.

These data are then processed through POM’s AI Analysis & Prediction engine, which analyzes large-scale real-world healthcare data to identify health risks, detect abnormal trends and generate predictive insights before diseases progress.

The resulting intelligence empowers physician intervention, enabling healthcare professionals to deliver more personalized recommendations, timely medical interventions and continuous health management supported by objective, real-time data.

POM further extends this ecosystem through insurance-healthcare integration, collaborating with healthcare payers to improve disease prevention, optimize healthcare resource allocation and facilitate the delivery of value-based healthcare services.

As more users join the platform, additional real-world healthcare data continuously enhances AI performance, improves healthcare services, attracts new ecosystem participants and reinforces a self-sustaining cycle of innovation and long-term growth.

Management Commentary

Mr. Zhenyang Shi, Chairman and Chief Executive Officer of POMDOCTOR, commented: “Healthcare is entering a new era in which continuous data, artificial intelligence and proactive intervention will fundamentally reshape the way healthcare is delivered. We believe the growing demand for chronic disease management, together with advances in AI, the widespread adoption of wearable devices, and the evolution of healthcare payment models, is creating a significant opportunity to transform healthcare from reactive treatment to predictive health management.

Our strategic upgrade reflects our long-term vision of building the infrastructure that powers predictive healthcare rather than simply providing individual healthcare services. By integrating wearable technologies, AI, physician resources, healthcare payment networks, and real-world healthcare data into one intelligent platform, we aim to create a sustainable healthcare ecosystem that delivers long-term value for patients, healthcare providers, insurers, pharmaceutical companies and shareholders.

Looking ahead, we will continue building upon our strong foundation in China while advancing our international growth strategy, with the United States representing an important next step in our global development. As we expand our technology capabilities, strategic partnerships, and healthcare ecosystem across key markets, we remain committed to making predictive healthcare more accessible worldwide and building a globally connected healthcare infrastructure for the future.”

About POMDOCTOR LIMITED

POMDOCTOR LIMITED (NASDAQ: POM) is a digital healthcare company focused on advancing AI-enabled healthcare solutions and expanding predictive healthcare capabilities. The Company leverages physician resources, wearable technologies, artificial intelligence, healthcare payment networks and real-world healthcare data to support more personalized, continuous and data-driven healthcare services. Building upon its established healthcare platform and physician network, POM is pursuing the development of a predictive healthcare data and services infrastructure designed to improve healthcare outcomes and create long-term value for patients, healthcare providers and other ecosystem participants. For more information, please visit the Company’s website: http://ir.7shiliu.com.

Forward-Looking Statements

Certain statements in this announcement are forward-looking statements. These forward-looking statements involve known and unknown risks and uncertainties and are based on the Company’s current expectations and projections about future events that the Company believes may affect its financial condition, results of operations, business strategy and financial needs. Investors can find many (but not all) of these statements by the use of words such as “approximates,” “believes,” “hopes,” “expects,” “anticipates,” “estimates,” “projects,” “intends,” “plans,” “will,” “would,” “should,” “could,” “may” or other similar expressions. The Company undertakes no obligation to update or revise publicly any forward-looking statements to reflect subsequent occurring events or circumstances, or changes in its expectations, except as may be required by law. Although the Company believes that the expectations expressed in these forward-looking statements are reasonable, it cannot assure you that such expectations will turn out to be correct, and the Company cautions investors that actual results may differ materially from the anticipated results and encourages investors to review other factors that may affect its future results in the Company’s filings with the SEC.

For more information, please contact:

POMDOCTOR LIMITED
Investor Relations Department
Email: ir@7lk.com

Ascent Investor Relations LLC
Tina Xiao
Phone: +1-646-932-7242
Email: investors@ascent-ir.com

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SOURCE POMDOCTOR LIMITED

LawZero’s research team, led by Yoshua Bengio, makes the mathematical case for a “disinterested” AI that predicts the truth without pursuing goals of its own. 

MONTRÉAL, July 2, 2026 /CNW/ – LawZero, a nonprofit dedicated to safe-by-design artificial intelligence (AI), today released a paper providing a new mathematical framework, representing a fundamental shift in the development of safe AI: one built to make honest predictions about the world without pursuing goals of its own.

Titled: “Safety from Honesty in a Disinterested AI Predictor” and authored by a team led by Yoshua Bengio, the work tackles what many researchers now consider a major danger of increasingly capable AI: that systems trained to imitate people and optimize for outcomes can quietly become goal-directed in ways their designers never intended. Today’s most powerful AI systems learn first by imitating vast amounts of human-written text, and then by being rewarded for the answers approved by the users.

Today’s most powerful AI systems learn first by imitating vast amounts of human-written text, and then by being rewarded for the answers approved by the users. The paper argues that this training recipe can inadvertently incentivize systems to pursue unwanted goals of their own — arising either from imitating human drives or wanting to maximize approval. This structural pressure can manifest as harmless-seeming flattery, or scale into highly critical safety risks such as deception or resistance to being shut down. The authors call this “implicit agency”: goal-seeking that no one asked for and that may not even be visible in the system’s stated answers.

“Most AI today is trained to act like us, to imitate, to please”, said Yoshua Bengio, Co-President and Scientific Director at LawZero. “We’re building something different: a system that mechanically applies the scientific method for hypothesizing and predicting, trying to understand the world and report its beliefs honestly, including about what might harm us. Such a disinterested, scientist-like AI observes and analyzes rather than having hidden drives that can lead to scheming”, Bengio concluded.

A scientist, not an agent. 

The proposed alternative is to build AI that behaves like a scientist reporting their best explanatory theories rather than act like an agent. A scientist tries to understand and predict the world accurately; an agent tries to change it to get what it wants. LawZero’s “Scientist AI” predictor is trained only to estimate the probability of events through the most broadly explanatory hypotheses, and is given no incentive to influence what happens next as a consequence of its predictions, a property called consequence invariance. The paper calls this Scientist AI system disinterested; it has no stake in the outcomes its predictions bring about.

Two design choices do the work. First, the system is taught to distinguish “someone claimed X is true” from “X is true” so it can learn from human text, by trying to explain it rather than imitate it, i.e., without absorbing human goals and biases as if they were established facts.

Second, and this is the heart of the safety case, the training process never rewards the system for the real-world consequences of its answers, only for the explanatory power of its hypotheses, avoiding the feedback loop that would otherwise teach it to manipulate. When the broader system needs to take actions, such as searching or using tools that work is handled by separate, auditable code with a safety guardrail that withholds any answer it judges to be too risky.

Why accuracy and safety reinforce each other.

The paper’s central result is a mathematical argument that, under clearly stated conditions, the chance of training such a system into a dangerous one is extremely small. Causing serious harm would require the system to be dishonest in a coordinated, sustained way across many separate answers — yet the training method provides no push toward that, and the objective directly penalizes the kind of miscalibration it would demand. The striking conclusion: accuracy and safety reinforce one another. The very honesty that makes the system useful is also what makes deception extremely unlikely, meaning there is no trade-off between accuracy and safety.

“The safety provided by the Scientist AI and its honest predictions makes it the ideal solution for monitoring and guard-railing frontier AI systems,” explained Iulian Serban, Senior Director, Research & Development at LawZero. “By analyzing the actions, responses and history of other AI systems, the Scientist AI will more accurately and honestly evaluate whether their actions and responses may cause harm and, if so, block them.” 

In addition to deploying the Scientist AI as a safety guardrail, LawZero expects the Scientist AI to act as a research acceleration tool providing hypothesis generation and probabilistic reasoning capabilities, helping researchers make new discoveries across fields ranging from medicine and climate change to cybersecurity and AI safety itself.

The authors are careful about scope, however: the paper’s argument addresses one specific risk ― the predictor itself developing hidden goals ― and is a formal case resting on assumptions, not an absolute guarantee. It does not by itself cover deliberate human misuse, one-off honest mistakes or the safety of larger, more capable agentic systems built on top of the predictor. However, agentic extensions are precisely the directions of current research at LawZero. The team presents the work as a foundation for safer AI and proposes concrete experiments to test its assumptions empirically.

Read the complete case. The full argument, including the formal proofs, the consequence-invariance result, and the experiments LawZero proposes to test it, is available here. 

About LawZero

LawZero is a nonprofit organization committed to creating technical solutions that enable safe-by-design AI systems. Its scientific direction is based on new research and methods led by Professor Yoshua Bengio, the most cited AI researcher in the world. Based in Montréal, LawZero aims to build a safe-by-design AI that could be used to accelerate scientific discovery, to provide oversight for agentic AI systems, and to advance the understanding of AI risks and how to avoid them. The organisation aims to cultivate AI as a global public good–developed and used safely towards human flourishing. LawZero was incubated at Mila – Quebec AI Institute, a nonprofit academic research institution founded by Professor Bengio.

SOURCE LawZero