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For more than two decades, Microsoft Research has worked at the intersection of computation and biology. Our research has spanned immunology, virology, genomics, biomedical imaging, cell biology, and protein engineering. That work has produced foundational methods, new science, and technology that reached the clinic, from rare and infectious disease diagnosis to cancer biomarker detection.

Across that work, one lesson has become increasingly clear: biology does not divide itself into the neat boundaries our models and tools often do. Genes influence proteins; proteins interact within cells; cells organize into tissues; and experiments continually reshape what scientists know and what they choose to ask next. Making progress on the hardest biological questions therefore requires more than increasingly capable models of individual datasets or tasks. It requires systems that can connect knowledge across scale and modalities, reason about experiments and evidence, and participate in the iterative process through which science advances. 

Today, Microsoft Research is introducing Quine (opens in new tab), a research effort designed to work across those boundaries, reflecting our long-term vision for a discovery system that evolves through scientific use. Quine brings together a world model of biology with a harness that connects scientific tools, literature, the wet lab, and the researchers using them.

The limits of experimentation

Even as experimental techniques have improved and wet-lab throughput has increased, biology remains fundamentally constrained by time and complexity. Nature cannot be rushed, nor can it be derived from first principles. Experiments are slow, iteration cycles are long, and many of the most important questions involve interactions, combinatorial design spaces, and downstream effects that are simply too large to explore experimentally alone.

At the same time, advances in large-scale machine learning, particularly the emergence of general-purpose foundation models and reasoning models that can iteratively work through problems, suggest a new possibility. These systems are beginning to demonstrate capabilities beyond pattern recognition: integrating information across domains, reasoning over abstractions, and supporting iterative problem-solving. Just as importantly, many of the techniques developed for human language have proven remarkably adaptable to aspects of biology, enabling models to learn representations of biological systems across diverse data types and scales.

This raised a provocative question for us: what would it take to build a world model for biology? By world model, we mean a system that can represent the state of a biological system, predict how that state will evolve in response to interventions, and reason over the consequences of those interventions multiple steps into the future. Such a system would not replace experimentation. Rather, it would allow us to use computation to explore, propose, rank, and prioritize potential paths forward before committing scarce laboratory resources. Our north star is a future in which scientists, models, and experiments operate in a continuously accelerating loop.  

What we’ve built

Quine is our first step toward that vision. It includes both a world model of biology and a harness that connects the model to orchestration and reasoning models, scientific tools, the literature, and the teams of scientists using them.  

Core to this effort is the world model, which learns shared representations across biological modalities and scales, including sequence, structure, function, cellular state, and imaging data. By training across these modalities jointly, Quine can use evidence from one modality to inform predictions in another, capturing relationships that would otherwise remain siloed or lost when separate single-domain specialist models are orchestrated by the system. This multimodal design reflects the reality of biology: understanding any one level requires context from the others. Rather than diluting performance, we find that learning across these connected representations strengthens it, enabling the model to generalize more effectively and to support a broader range of biological reasoning tasks.

By integrating this model in a unified system that facilitates computational experimentation and learns from iteration, researchers can generate predictions across modalities and reason about the consequences of biological interventions before they are tested in the lab. Critically, the world model doesn’t need to be perfect—indeed, it will never perfectly model biology—it simply needs to usefully inform experimental design. 

What this looks like in cancer biology

A concrete example comes from our work on pancreatic ductal adenocarcinoma (PDAC), the most common form of pancreatic cancer and one of the most challenging to treat. In collaboration with researchers at the Broad Institute of MIT and Harvard, we have spent years developing and applying patient-derived ex vivo models to investigate a longstanding hypothesis: that tumor behavior and drug response depend not only on genetics, but also on transcriptional cell state. In PDAC, tumor cells can occupy different cellular states associated with how they respond to treatment.  

With Quine, we have now put that hypothesis into practice, exploring whether non-genetic features of cancer, particularly cellular state, can serve as actionable therapeutic targets. We used Quine to predict and prioritize thousands of compounds based on their potential to shift tumor cells between therapeutically relevant states. In wet-lab studies focused on the transition from classical to basal cell states, Quine’s highest-ranked compounds produced the largest intended shifts across experimental assays. The entire process—from rapidly narrowing the compound search space to prioritizing a handful of promising candidates to be validated in the lab—took just one weekend, potentially saving months of experimental work and significant research costs. Notably, some of the strongest effects came from compounds with unexpected mechanisms of action, offering early evidence that AI can uncover new opportunities for drug repurposing and discovery. 

While the reverse state transition proved more difficult (from basal to classical cell states), Quine predicted that available compounds would have this weaker effect. More notably, the experiments revealed something we did not fully anticipate: Quine predicted that several compounds would consistently move cells toward a distinct third phenotype—an observation borne out in the lab—suggesting that the pancreatic cancer cell-state landscape is richer than a simple classical-basal axis. The experiments not only tested the model’s hypotheses but also generated new ones. 

Our continued work here will use newly integrated RNA datasets and tasks to represent this richer landscape, strengthen state-transition predictions, and provide calibrated confidence estimates that help scientists prioritize the most promising hypotheses for wet-lab testing. And this is exactly the feedback loop Quine was built to support. Each round of experimentation improves our understanding of biology, and each new insight can become training signal for the next generation of the system.  

Learning through discovery – an invitation

We created Quine to improve how we do science ourselves. As the system evolved, we embedded it in our ongoing scientific programs. Every research question, experimental result, and unexpected finding became an opportunity to refine the models, improve the workflows, and better understand where AI could meaningfully accelerate our research. Over time, the system became central to how we approached discovery across multiple domains, including in cancer biology, protein engineering, genomics, and bioimaging.

We’d like to continue building on that foundation by introducing Quine at this early stage. Quine grew out of our own research, but we don’t want its continued development to happen in isolation. Many of the questions that will determine where it goes next are not ones we can answer alone; they require the expertise, creativity, and perspectives of the broader scientific community. For this reason, we’re opening the Quine Fellows program to put the system directly into the hands of scientists working at the frontier of biology and medicine.

Building responsibly will always come first. We believe progress in AI and biology must go hand in hand with safety, security, and responsible stewardship. We are taking a deliberate, phased approach to Quine’s development and access, with initial availability limited to the Quine Fellows program and select research collaborations. Ongoing internal review and built-in safeguards will help us maintain appropriate oversight as the system evolves. As the technology matures, we expect to expand access through products like Microsoft Discovery (opens in new tab).

It’s tempting to frame progress in AI for biology around benchmarks and leaderboards. Those matter, but the real test of Quine is what happens when it encounters science as it’s actually practiced. Can it be useful when evidence is incomplete? When facing questions genuinely never asked before? Can it help make the path from questions to discovery shorter?

Ultimately that’s the standard we care about. The ambition is for Quine to recede into the background of scientific practice, while the science moves faster in the foreground. The protagonists are not the model or the platform. They are the scientists, the experiments, and the discoveries that follow. 

On July 24, 2025, Microsoft Research Asia – Singapore (MSRA – Singapore) opened its doors as Microsoft’s first research lab in Southeast Asia. The launch built on more than two decades of collaboration between Microsoft Research Asia and Singapore’s universities, research institutes, and government agencies. It marked a shift to a closer, more integrated relationship aimed at supporting Singapore’s innovation ecosystem and advancing the development and beneficial use of AI.

“When we established Microsoft Research Asia — Singapore one year ago, our vision was not simply to open another research lab. It was to build a bridge connecting Microsoft’s frontier AI research with Singapore’s world-class universities, forward-thinking government agencies, and dynamic industry ecosystem. One year in, what gives me the greatest confidence is not just the research we have produced, but the depth of partnership we have built. That is the foundation on which lasting impact is built.”

— Dr. Lidong Zhou, corporate vice president at Microsoft and managing director of Microsoft Research Asia

Established in 1998, MSRA has a presence in Beijing, Shanghai, Vancouver, and Tokyo, along with Singapore. The Singapore lab strengthens the collaboration with local partners and extends the scientific and societal impact of Microsoft’s global research community. 

Over the past year, the Singapore lab has grown its team, deepened partnerships, and explored how frontier AI research can create real-world value. Guided by its mission to advance frontier AI for humanity, its work spans four pillars: next-generation AI models and agentic systems, domain-specific AI for real-world impact, AI-native research practices, and ecosystem and talent development.

From frontier research to real-world impact – and back

Research has its greatest impact when it moves beyond the laboratory into practice: in health and medicine, enterprises, and the daily operations of organizations solving real problems. MSRA – Singapore aims to do just that through a two-way exchange: partners bring domain expertise, real-world data, and operational challenges, while the lab contributes frontier research and engineering capabilities. Together, they translate fundamental advances into practical solutions, while surfacing new questions that feed back into research in a kind of research-to-impact flywheel. Advances in AI foundations enable more capable models and agentic systems that can reason, plan, use tools, and collaborate with people. These capabilities create new opportunities for AI-native innovation. In turn, real-world applications reveal limitations and generate new questions, feeding insights back into foundational research and sustaining a continuous cycle of discovery, application, and learning.

Healthcare offers an early example of the lab’s research-to-impact model. Research on multimodal healthcare AI and self-evolving diagnostic agents is deployed and extended through partnerships across Singapore’s healthcare ecosystem. The lab is collaborating on multimodal and agentic AI approaches to support clinical decision-making, unlock insights from complex medical data, and advance research in disease diagnosis, risk assessment, and personalized care, with the longer-term goal of enabling meaningful real-world use.

Beyond healthcare, the lab is working with organizations across financial services, education, and technology to explore how frontier AI can address sector-specific needs and operational challenges.

“Over the past year, the lab has become an integral part of both Microsoft’s local ecosystem and Singapore’s broader innovation community. Its progress demonstrates the value of establishing a sustained research presence in Singapore.”

— Wee Luen Chia, managing director, Microsoft Singapore

Singapore has made AI a national priority through its National AI Strategy 2.0 (opens in new tab), which focuses on deploying industry-transforming AI, advancing frontier research in AI foundations, and developing responsible, socially beneficial applications. These priorities span areas including healthcare, education, finance, and Smart Nation (opens in new tab) services. MSRA – Singapore’s research portfolio aligns with these priorities, with a particular focus on building AI systems that are reliable and trustworthy across Southeast Asian contexts. 

The Singapore Economic Development Board (EDB) has been a foundational partner. It supported the lab’s establishment and continues to support joint PhD training through the Industrial Postgraduate Programme (IPP). The relationship has since grown into strategic collaboration. In February 2026, EDB co-hosted an Executive Roundtable on AI in Logistics and Transportation, bringing together more than ten regional industry leaders to explore sectoral challenges.

“Singapore is building deep research capabilities to push the boundaries of AI and other frontier technologies. Microsoft Research Asia – Singapore is an important partner in this effort. Since its launch in 2025, the lab has brought Microsoft’s global research expertise closer to our ecosystem, partnering academia, industry, and government to advance frontier AI research and translate it into real-world applications in areas such as healthcare. MSRA is also developing the next generation of AI research talent who can create meaningful impact for our economy and society.”

— Low Jin Yong, senior vice president and co-head, Digital Industry Singapore

Government engagement extends beyond EDB. In July 2025, IMDA (opens in new tab) co-hosted Microsoft Research Asia Singapore Day (opens in new tab) — Advancing Industrial AI, a full-day event spanning healthcare AI, interactive world simulators, AI agents, and societal AI. MSRA – Singapore has also engaged with the Ministry of Trade and Industry and the Ministry of Health. 

In addition to its deep ties with government agencies, MSRA has built strong connections with Singapore’s academic community, supporting more than 75 research projects with Singapore institutions since 2004. The opening of the Singapore lab has brought new engagements, with nine new projects in its first year with National University of Singapore (NUS) and Nanyang Technological University, Singapore (NTU) across healthcare AI, robotics, AI systems, and multi-agent systems. 

A landmark example is the five-year Framework Research Agreement (opens in new tab) with NUS, announced in 2025, which provides a platform for joint AI research, talent development, and academic exchange. It defines shared research priorities in healthcare, societal AI, spatial intelligence, and data-intensive computing alongside an annual NUS–MSRA Academic Symposium that brings researchers from both organizations together to shape the collaboration agenda. 

“This partnership creates new opportunities for frontier research, talent development, and knowledge exchange, strengthening connections across academia, industry, and the broader innovation ecosystem.”

— Professor Liu Bin, deputy president (Research and Technology), National University of Singapore

The collaboration is already producing concrete research across several frontier areas. NUS Assistant Professor Mike Shou and MSRA’s Xinxing Xu are developing unified multimodal AI for healthcare. NUS assistant professor Yang You and MSRA distinguished scientist Furu Wei are studying systems foundations for diffusion-based language models. NUS assistant professor Jialin Li is working with MSRA on rigorous distributed agreement in multi-agent systems and verifiable machine learning systems.

“Whether during my visit to the Systems Research Group in Beijing or through our ongoing collaborations, I have been impressed by how deeply MSRA researchers engage with difficult problems and how openly they exchange ideas. For young faculty and students, it is a rare environment to explore ambitious ideas.“

— Jialin Li, Sung Kah Kay Assistant Professor, School of Computing, National University of Singapore, MSRA StarTrack Scholar

The NUS–Microsoft Research Asia AI Summer School has reached more than 300 students since its launch in 2025, attracting participants from Singapore, Southeast Asia, and beyond. Through three days of lectures, hands-on workshops, and small-group discussions, the program creates a forum for researchers and students to engage with emerging AI questions and scholars shaping the field. The 2025 program featured a keynote by Doug Burger, technical fellow and corporate vice president of Microsoft, on the AI revolution. Ahead of the 2026 Summer School, Lidong Zhou delivered a lecture on system intelligence, helping frame the conversations that followed. 

“I have seen the NUS–MSRA partnership grow through sustained collaboration in AI research and talent development. What makes this partnership especially meaningful is the opportunity to bring together researchers and students from both communities to learn from one another, pursue new ideas, and create lasting impact together.”

— Tulika Mitra, dean, NUS School of Computing

Beyond NUS, Microsoft Research Asia — Singapore has built connections across Singapore’s research community, including with NTU, Singapore Management University (SMU) (opens in new tab), Agency for Science, Technology and Research (A*STAR), and other institutions. Research arising from these partnerships has already been published at leading venues, including the International Conference on Learning Representations (opens in new tab) (ICLR) and the Conference on Language Modeling (opens in new tab) (COLM). 

From early exposure to research leadership: Cultivating Singapore’s next generation of AI talent

For Microsoft Research Asia, talent development is not a standalone program but a long-term investment in Singapore’s research and innovation capacity, supporting a comprehensive talent pipeline spanning internships, joint PhD supervision, visiting scholar programs, and fellowship initiatives. Through these opportunities, students and early-career researchers gain access to world-class mentorship, a curiosity-driven culture, frontier research challenges, and a global network connecting academia, industry, and real-world applications.

What makes this model distinctive is its continuity. Students may engage with Microsoft Research Asia through workshops or summer schools, gain hands-on experience through its flagship research initiative, Microsoft Research Asia Stars of Tomorrow Internship Program, or pursue doctoral research through joint PhD programs. Some participants later return as faculty collaborators, mentors, or research leaders. Together, these programs form a connected pathway that supports researchers at successive stages of development. 

The IPP represents a sustained form of collaboration, enabling PhD students to conduct long-term research under joint mentorship from academia and industry. Qiming Huang, the first IPP student at MSRA — Singapore, is co-supervised by NUS professor Mike Shou and MSRA researcher Xinxing Xu. Through the program, Huang worked closely with researchers from both institutions, gaining exposure to academic rigor and industry-driven research. At CVPR 2026 (opens in new tab), one of the world’s leading conferences in AI and computer vision, Qiming Huang’s paper on RobotSeg – a foundation model for robot segmentation in image and video – was selected for an oral presentation.  

“Working alongside researchers from both Microsoft Research Asia and NUS changed how I approach AI research. Beyond technical guidance, I learned to frame ambitious questions, collaborate across disciplines, and think about the real-world impact of research

— Qiming Huang, IPP student from National University of Singapore

NTU assistant professor Jianfei Yang, embodies the continuous collaborative opportunities enabled by Microsoft Research Asia. As a student, Yang’s team won the Beauty of Programming competition, an experience that inspired him to pursue a PhD and begin an academic career. Over the years, he watched many of his classmates and close friends grow into outstanding researchers through MSRA programs. Today, he collaborates with MSRA researchers in robotics and jointly supervises students through the IPP, creating for the next generation the same opportunities that inspired his own journey.

“Although I never interned at Microsoft Research Asia myself, it always felt close. Having the opportunity to work alongside a research lab that inspired so many researchers of my generation, and now to co-mentor students together, is both meaningful and rewarding.”

— Jianfei Yang, assistant professor, director of Multimodal embodied AI and Robotic Systems (MARS) Lab, Nanyang Technological University

NTU associate professor Ziwei Liu, himself a former MSRA intern, recently saw one of his students become the only Asia-based recipient of the Microsoft Research global Fellowship award for 2025–2026. 

“As a former intern, I have seen how an environment that brings together world-class researchers, open scientific exchange, and ambitious young talent can shape the next generation of researchers. It is especially meaningful to see my own students now benefit from these opportunities.”

— Ziwei Liu, associate professor, College of Computing and Data Science, Nanyang Technological University

The Stars of Tomorrow Internship Program has engaged more than 90 students from Singapore-based institutions over the years. For many students, these internships serve as a gateway to frontier research and help bridge the gap between academic study and real-world innovation.

For exceptional doctoral students, the Microsoft Research Asia Fellowship provides another pathway for growth, recognition, mentorship, and access to Microsoft’s global research community. Since the program began, 13 students from Singapore-based universities have received the fellowship, many of whom have gone on to become professors, industry research leaders, and technology entrepreneurs around the world.

The next phase: Scaling what works

As Microsoft Research Asia — Singapore enters its second year, the focus is shifting from building foundations to scaling the strongest models of collaboration and demonstrating their impact more broadly.

One year ago, the lab opened with a mission to advance frontier AI in close partnership with the ecosystem around it. That mission is already taking shape through joint research, talent development, and the first partnerships connecting lab work to real-world challenges.

The year ahead is about scaling what works. By deepening these connections, Microsoft Research Asia — Singapore can bring more frontier research into practice and create meaningful value for Singapore, the wider region, industry, and society. 

Readily-available physical AI, with robotics assisting users in manufacturing, home, and warehouses scenarios, holds immense potential to improve safety, productivity, and assistance across a wide range of tasks. In many ways, AI for the physical world represents a major frontier for AI . Physical AI must operate in open, unpredictable environments, interact with both other robots and people, and work with a diversity of embodiments. Realizing this vision requires advances along three dimensions: robot hardware, embodied AI models, and systems infrastructure for training and inference. While robot hardware and the AI models have advanced rapidly in recent years, we turn our focus on a relatively under-addressed aspect: inference infrastructure of physical AI. Enabling robots to effectively and safely operate in the physical world will require sophisticated systems to handle large volumes of distributed inference compute.

Today, the prevailing approach to physical AI is to provision a GPU onboard the robot, e.g., by wiring a GPU to the robot. In this model, the robot’s inference will be confined to the onboard GPU, and provide the robot with the necessary chunks and sequence of actions for the execution of its tasks. While higher-level planning may be performed in the cloud, task execution typically remains tied to the robot itself. We challenge this assumption.  As physical AI models grow in size and sophistication, the constraints of onboard compute become increasingly apparent. GPUs consume significant power, reduce battery life, add cost and weight, and can limit the ability to run the latest generation of AI models. 

To better understand the systems implications of physical AI, we conducted the first systematic study of robotics workloads. We focused on mobile robotic manipulation, with the canonical task such as “check for rubbish in the kitchen and put it in the trash.” Such a task involves planning the path to the kitchen, perceiving the environment to find rubbish, navigating to the rubbish, picking up the rubbish, and navigating back to the trash can for disposal. We evaluated representative models across three core capabilities: semantic mapping and planning, navigation, and manipulation, as summarized in Figure 2. 

Offloading physical AI inference out of the robot improved its response time and accuracy, along with battery lifetime and cost. We evaluated the inference models across a range of onboard, edge, and cloud compute configurations. Details of the specific test hardware are available in our technical report. 

Benefits in task performance: Our evaluation shows offloading inference can significantly improve robot performance across mapping, planning, navigation, and manipulation workloads. Some smaller GPUs could not accommodate the mobile manipulation stack. On GPUs with sufficient memory, mapping and planning slowed by up to 383% compared to an A100, thus limiting the robot’s abilities in dynamic spaces. Navigation showed a 30% drop in its timely detection of obstacles with lighter GPUs. While the VLA models did not dramatically slow down with smaller GPUs, the slowdown was still sufficient to drop their accuracies by 50%. In other words, onboard GPUs limited the performance of the robots while offloading their inference to an on-premise or cloud GPU boosts their operations, as shown in the videos below and quantified in the graphs. As physical AI models continue to grow in size and complexity, the benefits of offloading are likely to become even more pronounced.

Benefits in battery lifetime: Beyond performance, onboard GPUs also significantly drained the battery life of the robot. We compared the increase in battery lifetime by replacing an onboard GPU with a Raspberry Pi-5 board and shipping all the data to the offloaded GPU. The larger onboard GPUs, such as Jetson Thor, drained robot batteries by up to 160% (or a few hours) for even the larger robots.

The above results show that offloading GPU inference out of the robot is critical for functioning in the open world with large models and long battery lifetimes. Nonetheless, offloading inference out of the robot involves a complex tradeoff involving performance, network latency and bandwidth, and available GPU resources. We believe that our measurement study will inform the design of physical AI inference systems.

We have built a toolset for easy inference offloading out of the robot and distributing inference between the edge GPU and cloud. Kubernetes is a natural platform to provide a uniform abstraction to distribute robotic AI between the robot’s compute, edge GPU, and overflowing to the cloud. The toolset allows automatic containerization and offloading of robotics workloads using declarative specifications, distributes physical AI containers with smart policies using Kubernetes, and integrates with robotic simulators, LeRobot, and ROS2 for easy development. The sequence of steps below shows how the toolset can be prompted with what to offload, and how it creates a separate container for GPU inference and offloads the same.

Microsoft has recently released the Physical AI Toolchain (opens in new tab) for operationalizing physical intelligence at scale. Physical AI Toolchain is an open-source, production-ready framework that integrates Microsoft Azure (opens in new tab) cloud services with NVIDIA’s (opens in new tab) physical AI stack, accelerating robotics and physical AI developers to automate and scale data curation, augmentation, and evaluation across perception, mobility, imitation learning, and reinforcement learning pipelines. We are announcing the addition of an industry-first capability for offloaded physical AI inference for robots as part of the Physical AI Toolchain. This release includes example projects for offloading inference of a SO-101 and a UR10e. The videos below show the offloading of the inference of Microsoft’s Rho model, targeted at dual-arm robots, to a Jetson Thor GPU, which controls the actions of the Mobile Aloha robot (opens in new tab).  

Check out the inference offload feature, look into the source code, and let us know your feedback. We have already tested it with many real-world use cases, and look forward to hearing about your deployment experiences.