Advancing AI: Highlights from January

January set the tone for the year ahead at Sony AI, with work that spans foundational research, scientific discovery, and global engagement with the research community.

This month’s highlights reflect a common thread: clarity. From rethinking how scientific hypotheses are evaluated, to publishing a unifying reference on diffusion models, to sharing new perspectives on learning in autonomous systems, our teams focused on making complex ideas more interpretable, grounded, and useful. January also looked ahead, with early signals of what’s to come at major conferences and continued momentum across responsible and creative AI research.

Below, we take a closer look at the work that shaped the start of 2026.

Reimagining How Researchers Evaluate Scientific Hypotheses

In January, Sony AI published a deep dive into how its Scientific Discovery Team is rethinking hypothesis generation in an era of overwhelming scientific output.

Rather than treating research papers as isolated documents, the team approaches the scientific literature as a living, interconnected system. By modeling how ideas emerge, evolve, and reinforce one another over time, their work aims to uncover scientifically plausible connections that are implied by decades of research — but not yet explicitly stated.

At the center of this effort is literature-based hypothesis generation, powered by temporal knowledge graphs that track how concepts and relationships shift year by year. This structure allows models to surface transparent, literature-grounded reasoning paths, offering researchers interpretable insights rather than opaque predictions.

The article also explores the practical challenges of working with inconsistent, contradictory scientific data, and how constraints around compute and usability shaped the system’s design. Ultimately, the team emphasizes that the goal is not to replace scientific intuition, but to expand researchers’ field of view — helping them ask better questions, navigate vast bodies of knowledge, and identify promising directions more efficiently.

Dive into the interview here: How Sony AI’s Scientific Discovery Team is Reimagining How Researchers Evaluate Hypotheses

AAAI 2026 Recap

At AAAI 2026, Sony AI presented research centered on reliability, interpretability, and real-world performance. The work spans continual learning, robust decision-making under change, and creator-focused methods for editing audio, video, and speech — without losing intent or structure.

A common theme runs through these contributions. AI systems must behave consistently as conditions shift. They must retain knowledge over time. They must generalize beyond the settings they were trained in.

Across domains, the research moves past isolated benchmarks and toward practical use. Whether improving long-term learning, strengthening generalization, or supporting creative workflows, the focus is the same: AI that is more dependable, more controllable, and better suited for real-world deployment.

Sony AI's Contribution at AAAI 2026

Coming Soon

Looking ahead, Sony AI will be well represented at ICLR 2026, with more than 10 papers accepted across generative modeling, diffusion, multimodal representation learning, and creator-focused AI systems.

The accepted work spans topics from visual–interactive embeddings and concept-level understanding in diffusion models to efficient flow-based training methods, scene-consistent video generation, and AI-assisted music post-production. Together, these contributions reflect ongoing efforts across Sony AI to advance both the foundations of machine learning and its application in creative, interactive, and real-world settings.

More details on the accepted papers, authors, and presentations will be shared soon as the conference approaches.

Until then, please check out our work at ICLR from last year:
Sony AI at ICLR 2025: Refining Diffusion Models, Reinforcement Learning, and AI Personalization

Additionally, Sony AI’s Chief Scientist, Peter Stone, will be in attendance at SXSW 2026, participating in a panel on humanoid robots and presenting on reinforcement learning. Details can be found below:

Humanoid Robots Are Clocking In: Meet Who’s Putting Them To Work

Saturday March 14, 2026 | 11:30 a.m. CT | JW Marriott - Salon D

You’ve seen the viral humanoid demos – now meet the people actually shipping them. From hospitals to warehouses, humanoid robots are already on shift. They ride elevators, open doors, and work alongside people in busy environments. But making that leap from prototype to real-world, reliable performance? It’s hard. This panel brings together renowned founders and researchers who’ve built and deployed some of the first real humanoids at scale. Join us for a behind-the-scenes conversation about what's working, what’s not, and what it really takes to get robots out of the lab and into the world.

Is Reinforcement Learning the Real Future of AI?

Saturday, March 14, 2026 | 4 p.m. CT | JW Marriott - Salon AB

While AGI, generative AI, and humanoid robots dominate the spotlight, reinforcement learning (RL) is quietly powering the next chapter of AI. Unlike other methods, RL enables systems to learn by doing – adapting through trial and error in dynamic environments. In this session, one of the world’s leading RL minds will explore why this approach is essential for building truly autonomous, intelligent systems, and how today’s breakthroughs are laying the groundwork for the next wave of transformative AI.

FHIBE Featured in Nature’s Research Community Blog

How do you evaluate fairness in computer vision when the benchmarks themselves are flawed?

In a new Behind the Paper post for the Springer Nature “Research Community Blog,” Alice Xiang, Global Head of AI Governance at Sony Group Corporation and Lead Research Scientist at Sony AI, reflects on the three-year effort behind FHIBE (Fair Human-Centric Image Benchmark).

The post examines why many existing benchmarks fall short, what it took to build a consent-driven and globally diverse dataset, and what FHIBE revealed when applied to face analysis, pose estimation, and foundation models.

Read her article here: https://bit.ly/4jQakrO
To learn more about FHIBE and access the benchmark, please visit: https://fairnessbenchmark.ai.sony/

Sony AI’s Imaging & Sensing Team Publish, “Principles of Diffusion Models”

In January, Sony AI researchers announced The Principles of Diffusion Models, a new book that offers a clear, unified view of how diffusion models emerged, and why today’s seemingly different approaches share the same mathematical foundations.

The book traces diffusion modeling back to its core idea: a forward process that gradually transforms data into noise, paired with a learned reverse process that reconstructs data step by step. It walks readers through three complementary perspectives (variational, score-based, and flow-based) showing how each formulation arises from the same underlying principles rather than competing design choices.

By framing diffusion models around a shared time-dependent velocity field and continuous generative trajectories, the monograph clarifies how modern techniques achieve controllability, efficiency, and flexibility. Later chapters explore guidance methods, numerical solvers for faster sampling, and diffusion-inspired flow models that map directly between points along the generative path.

Written for readers with a basic deep learning background, the book serves as both a conceptual reference and a stable foundation for further research, helping demystify diffusion models while connecting past formulations to current advances. The team hopes others will use this book in lectures at schools as well.

Access the book here: https://the-principles-of-diffusion-models.github.io/

Authors:

- Chieh-Hsin (Jesse) Lai (SonyAI)

- Yang Song

- Dongjun Kim

- Yuki Mitsufuji, PhD (SonyAI)

- Stefano Ermon

Peter Stone’s Invited Talk at AAAI 2026

Recently at AAAI 2026, Sony AI Chief Scientist, Peter Stone, delivered an AAAI Invited Talk, “From How to learn to What to learn in Multiagent Systems and Robotics.” exploring a shift underway in how researchers think about autonomy.

While recent years have brought rapid advances in machine learning algorithms and architectures, dramatically improving how systems learn, Stone argued that this progress alone is not enough. For autonomous agents operating in dynamic, uncertain environments, an equally critical challenge is determining what to learn: which concepts matter, which subproblems deserve attention, and how learning priorities should adapt over time.

This talk presents methods for identifying what to learn within the framework of reinforcement learning, focusing especially on applications in multiagent systems and robotics.

To watch the full talk, visit: AAAI Invited Talk, “From How to Learn to What to Learn in Multiagent Systems and Robotics.”

Connect with us on LinkedIn, Instagram, or X, and let us know what you’d like to see in future editions. Until next month, keep imagining the possibilities with Sony AI.