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WHO’s Historic Agreement: A Major Step Towards Global Pandemic Preparedness

In a groundbreaking move, members of the World Health Organization (WHO) have reached a historic, legally binding agreement aimed at preparing the world for future pandemics. This pact, designed to address the lessons learned from the COVID-19 crisis, sets the stage for a more equitable global response to health emergencies, particularly in the distribution of essential drugs, vaccines, and medical technologies.

The agreement marks a significant milestone in global health governance, especially at a time when multilateral institutions like the WHO are facing considerable financial strain. The United States, which was once the WHO’s largest financial contributor, withdrew from negotiations after President Donald Trump initiated the U.S.’s departure from the organization. Despite this setback, the deal underscores a strong commitment from member states to work together on global health security, with or without U.S. involvement. “This is a historic moment,” said Nina Schwalbe, founder of global health think tank Spark Street Advisors. “It demonstrates that countries are committed to multilateralism and to collective action.”

This agreement, the second of its kind in WHO’s 75-year history (the first being a tobacco control treaty in 2003), focuses on structural inequalities in how pandemic-related health tools are developed and distributed. Article nine of the deal ensures that future pandemic-related drugs, therapeutics, and vaccines will be made globally accessible. It also gives the WHO stronger oversight over medical supply chains and paves the way for local production of vaccines during health crises.

A key challenge in the negotiations was the issue of technology transfer—sharing the knowledge and manufacturing capabilities necessary for lower-income countries to produce their vaccines and treatments. To address this, the agreement mandates that manufacturers allocate at least 20% of their real-time production to the WHO during a pandemic, with a minimum of 10% designated for donation and the rest priced affordably for developing nations.

The deal is not yet finalized, as it must be adopted at the WHO Assembly in May, and some details, such as the annex on Pathogen Access and Benefit Sharing, still require further negotiation. However, once ratified, the agreement will bolster global preparedness, enabling quicker responses to future pandemics and more equitable access to life-saving resources.

As health experts emphasize, the global community must invest in preparedness now to avoid the costly toll of another pandemic. “We can’t afford another pandemic, but we can afford to prevent one,” said Helen Clark, co-chair of The Independent Panel for Pandemic Preparedness. This agreement represents a critical step toward ensuring that the world is better equipped to face future health crises with solidarity, transparency, and a commitment to equity.

Meta Unveils Muse Wonder Models In A Bid For Frontier AI Leadership

Meta has introduced two new AI models, Muse Wonder and Muse Wonder Full, signalling a more ambitious move into frontier AI. The releases build on Muse Spark but target more demanding tasks, including coding, while placing greater emphasis on long-term memory and personalised assistance.

Larger Models Target More Demanding Tasks

Unlike Muse Spark, which was designed for low latency and lightweight use, Muse Wonder and Wonder Full are significantly larger and, according to Meta, more capable. The company reports stronger benchmark performance and improvements on demanding workloads, including coding.

According to Meta’s model card, both models achieved benchmark results comparable to Fable 5.1 and GPT-6 Astra in the company’s internal testing. Meta described the release as “the next step on our scaling ladder and a major leap toward personal superintelligence” in its announcement.

Pricing Reflects Meta’s Frontier Ambitions

The models’ pricing places them in the premium AI segment. Through the Meta Model API, Muse Wonder will cost $4 per million input tokens and $20 per million output tokens, putting it in a similar pricing tier to GPT-6 Sol and Opus 5.5.

Muse Wonder Full, by contrast, will not be offered through the API. Meta says it will be exclusive to Muse Code subscribers, tying access to its most advanced model to a premium product. Both models will also power the Muse agent.

Long-Term Memory Takes Centre Stage

Meta is increasingly positioning its AI strategy around assistants that can support users over time rather than respond only to individual prompts. A central part of that approach is what the company calls “active memory”: the ability to retain, organise and synthesise information provided by users across extended periods.

Meta points to the MemoryBench benchmark, which evaluates systems designed for continual learning and memory construction. The company says Muse Wonder now outperforms Fable, Opus and Astra on this measure.

“We put a tremendous amount of effort into making sure your model can remember what’s important to you over days, weeks, months, and beyond,” research scientist Kavya Kopparapu said in a launch video.

New Models Follow Meta’s AI Reorganisation

The launch is also significant within Meta’s broader AI strategy. It is the company’s first frontier-model release since Scale AI founder Alexandr Wang reorganised its AI operations in August 2025. Meta has since introduced Muse Spark and the Muse agent, while making fewer high-profile model announcements than competitors such as OpenAI and Anthropic.

The new models were trained at Meta’s Prometheus data centre in central Ohio, which came online earlier in 2026, according to the company. The facility highlights the growing importance of infrastructure in the race to develop advanced AI systems, where performance depends not only on algorithms and data but also on computing capacity, data-centre investment and access to capital.

As competition intensifies, Muse Wonder and Wonder Full represent a broader bet on larger models, persistent memory and premium AI products. Their longer-term significance will depend on how their reported capabilities translate into real-world performance and whether users find enough value to pay for access.

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