Sony Research Awards

Sony Research Awards is sponsored by Sony. The Sony Research Award Program provides funding for cutting-edge academic research and helps build a collaborative relationship between faculty and Sony researchers. Both the Faculty Innovation Award and Focused Research Award create opportunities for university faculties and research institutions to engage in pioneering research that could drive new technologies.

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Sony Research Award Program # Sony Research Award Program * Sony's Purpose & Values * Creative Entertainment Vision # Sony Research Award Program * Sony's Purpose & Values * Creative Entertainment Vision SONY RESEARCH AWARD PROGRAM 2026 Sony Research Award Program is accepting proposals starting from July 15, 2026, until September 15, 2026. Details of the program, submission guidelines and terms are found in the links below.

* Faculty Innovation Award As part of one of the world's most innovative and recognizable brands, we are committed to support university research and innovation in the U.S., Canada, India, and select European countries, while also fostering partnerships with university faculty and researchers.

The Sony Research Award Program provides funding for cutting-edge academic research and helps build a collaborative relationship between faculty and Sony researchers.

With awards up to $150,000 USD* per year for each accepted proposal, both the Faculty Innovation Award and Focused Research Award create new opportunities for university faculties and research institutions to engage in pioneering research that could drive new technologies, industries and the future.

### Faculty Innovation Award Up to $100K USD* in funds to conduct cutting-edge research in Sony's general areas of interest### Focused Research Award Up to $150K USD* in funds to conduct research in the areas of Sony's immediate interest### Submission Guidelines Eligibility, requirements, submission protocol, and terms are explained in these guidelines.

### Application Window Proposal submission is open from July 15, 2026 to September 15, 2026. Congratulations to all award recipients in the Sony Research Award Program! We sincerely look forward to working closely with you.

* Professor Jiaqi Wang, Auburn University, United States * Professor Katerina Fragkiadaki, Carnegie Mellon University, United States * Professor Kostis Kaffes, Columbia University, United States * Professor Liang Zhao, Emory University, United States * Professor Wei Xu, Georgia Institute of Technology, United States * Professor Tianfu Wu, North Carolina State University Raleigh, United States * Professor Shanhui Fan, Stanford University, United States * Professor Xinyu Zhang, University of California San Diego, United States * Professor Ruohan Gao, University of Maryland, United States * Professor Gedas Bertasius, University of North Carolina Chapel Hill, United States * Professor Zezhou Cheng, University of Virginia, United States * Professor Yiyue Luo, University of Washington, United States * Professor Dimitrios Nikolopoulos, Virginia Polytech Institute State University, United States * Professor Hamed Rajabi, London South Bank University, United Kingdom ### 2024 (more renewals pending) * Professor Gopala Anumanchipalli, University of California, Berkeley, United States * Professor Gedas Bertasius, University of North Carolina at Chapel Hill, United States * Professor Pushpak Bhattacharya, Indian Institute of Technology Bombay, India * Professor Filippo Maria Fazi, University of Southampton, United Kingdom * Professor Ioannis Gkioulekas, Carnegie Mellon University, United States * Professor Boqing Gong, Boston University, United States * Professor Natasha Jaques, University of Washington, United States * Professor Chenfanfu Jiang, University of California, Los Angeles, United States * Professor Vaibhav Katewa, Indian Institute of Science, India * Professor Hun-Seok Kim, University of Michigan, United States * Professor Phillip Koehn, Johns Hopkins University, United States * Professor Karen Liu, Stanford University, United States * University of Texas at Austin, United States * Professor Owen Miller, Yale University, United States * Professor Ramesh Raskar, Massachusetts Institute of Technology, United States * Professor Craig Shultz, University of Illinois Urbana-Champaign, United States * Professor Haoning Tang, University of California, Berkeley, United States * Professor Andrea Vedaldi, University of Oxford, United Kingdom * Professor Lei Wang, Lancaster University, United Kingdom * Professor Ming-Hsuan Yang, University of California, Merced, United States * Professor Kwang Moo Yi, University of British Columbia, Canada * Professor Zhi-Li Zhang, University of Minnesota Twin Cities, United States * Professor David Lindell, University of Toronto, Canada** * Professor Haitham Al Hassanieh, EPFL, Switzerland * Professor Harry Atwater, California Institute of Technology, United States * Professor Changhyun Choi, University of Minnesota Twin Cities, United States * Professor Jia Deng, Princeton University, United States * Professor Rafael Gómez-Bombarelli, Massachusetts Institute of Technology, United States * Professor Laurie Heller, Carnegie Mellon University, United States * Professor Marco Hutter, ETH Zurich, Switzerland * Professor Ranjay Krishna, University of Washington, United States * Professor Johannes Levin, Ludwig-Maximilians-Universität München, Germany * Professor Yunzhu Li, Columbia University, United States * Professor David Lindell, University of Toronto, Canada * Professor David Lindell, University of Toronto, Canada*** * Professor Xuezhe Ma, University of Southern California, United States * Professor Alan Marshall, University of Liverpool, United Kingdom * Professor Euan McLeod, University of Arizona, United States * Professor Pratyush Mishra, University of Pennsylvania, United States * Professor Ken Nakagaki, University of Chicago, United States * Professor Sang-Hyun Oh, University of Minnesota Twin Cities, United States * Professor Allison Okamura, Stanford University, United States * Professor Hayder Radha, Michigan State University, United States * Professor Alan Ritter, Georgia Institute of Technology, United States * Professor Qi Sun, New York University, United States * Professor Molei Tao, Georgia Institute of Technology, United States * Professor Chen Wang, University at Buffalo, United States * Professor Nozomu Yachie, University of British Columbia, Canada * Professor Junsong Yuan, University at Buffalo, United States * Professor Yuke Zhu, University of Texas at Austin, United States * Professor Mohit Gupta, University of Wisconsin-Madison, United States** * Professor Chenfanfu Jiang, University of California, Los Angeles, United States** * Professor Ewold Verhagen, NWO Institute AMOLF, The Netherlands** * Professor Bolei Zhou, University of California, Los Angeles, United States** * Professor Pulkit Agrawal, Massachusetts Institute of Technology, United States** * Professor Psyche Loui, Northeastern University, United States** * Professor Mario Barbatti, Aix-Marseille Université, France * Professor Gedas Bertasius, University of North Carolina at Chapel Hill, United States * Professor Debashis Chanda, University of Central Florida, United States * Professor Angel Chang, Simon Fraser University, Canada * Professor Zaijun Chen, University of Southern California, United States * Professor Gregory Durrett, University of Texas at Austin, United States * Professor Jaime Fernandez Fisac, Princeton University, United States * Professor Chuang Gan, University of Massachusetts Amherst, United States * Professor Tatsunori Hashimoto, Stanford University, United States * Professor Chenfanfu Jiang, University of California, Los Angeles, United States * Professor Howard Lee, University of California, Irvine, United States * Professor Yong Jae Lee, University of Wisconsin-Madison, United States * Professor Yaping Liu, Northwestern University, United States * Professor Psyche Loui, Northeastern University, United States * Professor Michele Magno, ETH Zurich, Switzerland * Professor Alan Marshall, University of Liverpool, United Kingdom * Professor Julian McAuley, University of California, San Diego, United States * Professor Prineha Narang, University of California, Los Angeles, United States * Professor Dimitrios Nikolopoulos, Virginia Polytechnic Institute and State University, United States * Professor Sarah Ostadabbas, Northeastern University, United States * Professor Shinsuke Shimojo, California Institute of Technology, United States * Professor Bradley Siwick, McGill University, Canada * Professor Aneta Stefanovska, Lancaster University, United Kingdom * Professor Lili Su, Northeastern University, United States * Professor Ewold Verhagen, NWO Institute AMOLF, The Netherlands * Professor Nandita Vijaykumar, University of Toronto, Canada * Professor Yu Xiang, University of Texas at Dallas, United States * Professor Bolei Zhou, University of California, Los Angeles, United States * Professor Pulkit Agrawal, Massachusetts Institute of Technology, United States** * Professor Federico Capasso, Harvard University, United States** * Professor Aaron Courville, University of Montreal, Canada** * Professor Mohit Gupta, University of Wisconsin-Madison, United States** * Professor Aiichiro Nakano, University of Southern California, United States** * Professor Jose Principe, University of Florida, United States** * Professor Zhou Yu, Columbia University, United States** * Professor Michal Bajcsy, University of Waterloo, Canada * Professor Gedas Bertasius, University of North Carolina at Chapel Hill, United States * Professor Federico Capasso, Harvard University, United States * Professor Li-Jing Cheng, Oregon State University, United States * Professor Aaron Courville, University of Montreal, Canada * Professor Jia Deng, Princeton University, United States * Professor Guillermo Gallego, Technische Universität Berlin, Germany * Professor Aditya Grover, University of California, Los Angeles, United States * Professor Charles Hages, University of Florida, United States * Professor Cho-Jui Hsieh, University of California, Los Angeles, United States * Professor Dongyeop Kang, University of Minnesota Twin Cities, United States * Professor Pan Li, Georgia Institute of Technology, United States * Professor Jun Liu, University at Buffalo, United States * Professor Saturnino Luz, The University of Edinburgh, United Kingdom * Professor Arka Majumdar, University of Washington, United States * Professor Stephen Morris, University of Oxford, United Kingdom * Professor Aiichiro Nakano, University of Southern California, United States * Professor Andrew Owens, University of Michigan, United States * Professor Srijith P.

K, Indian Institute of Technology Hyderabad, India * Professor Bryan Pardo, Northwestern University, United States * Professor Arpita Patra, Indian Institute of Science, India * Professor Jose Principe, University of Florida, United States * Professor Ravi Ramamoorthi, University of California, San Diego, United States * Professor Arindam Sanyal, Arizona State University, United States * Professor Sebastian Scherer, Carnegie Mellon University, United States * Professor Alireza Vahid, University of Colorado Denver, United States * Professor Sheng Wang, University of Washington, United States * Professor Xiaolong Wang, University of California, San Diego, United States * Professor Benjamin Williams, University of California, Los Angeles, United States * Professor Lei Zhou, University of Texas at Austin, United States * Professor Pulkit Agrawal, Massachusetts Institute of Technology, United States** * Professor Mohit Gupta, University of Wisconsin-Madison, United States** * Professor Chia Wei Hsu, University of Southern California, United States** * Professor Deepak Pathak, Carnegie Mellon University, United States** * Professor Zhou Yu, Columbia University, United States** * Professor Jacob Andreas, Massachusetts Institute of Technology, United States * Professor Veselka Boeva, Blekinge Institute of Technology, Sweden * Professor Changhyun Choi, University of Minnesota Twin Cities, United States * Professor Steven Cummer, Duke University, United States * Professor Guido de Croon, Delft University of Technology, The Netherlands * Professor Krzysztof Gajos, Harvard University, United States * Professor Rafael Gómez-Bombarelli, Massachusetts Institute of Technology, United States * Professor Aman Haque, The Pennsylvania State University, United States * Professor Felix Heide, Princeton University, United States * Professor Chia Wei Hsu, University of Southern California, United States * Professor Kyle Jamieson, Princeton University, United States * Professor Evangelos Kalogerakis, University of Massachusetts Amherst, United States * Professor Patanjali Kambhampati, McGill University, Canada * Professor Bhaskar Krishnamachari, University of Southern California, United States * Professor Fenglong Ma, The Pennsylvania State University, United States * Professor Shuji Nakamura, University of California Santa Barbara, United States * Professor VP Nguyen, University of Texas at Arlington, United States * Professor Deepak Pathak, Carnegie Mellon University, United States * Professor Marc Pollefeys, ETH Zurich, Switzerland * Professor Fang Song, Portland State University, United States * Professor Natalie Stingelin, Georgia Institute of Technology, United States * Professor Gordon Wetzstein, Stanford University, United States * Professor Joerg Wrachtrup, University of Stuttgart, Germany * Professor Jun Yao, University of Massachusetts Amherst, United States * Professor Junming Yin, Carnegie Mellon University, United States * Professor Pulkit Agrawal, Massachusetts Institute of Technology, United States** * Professor David Bishop, Boston University, United States** * Professor Mark Brongersma, Stanford University, United States** * Professor Siyang Cao, University of Arizona, United States** * Professor Virginia de Sa, University of California, San Diego, United States** * Professor Mohit Gupta, University of Wisconsin-Madison, United States** * Professor Po-Chun Hsu, Duke University, United States** * Professor Pedro Lopes, University of Chicago, United States** * Professor Dimitris Papailiopoulos, University of Wisconsin-Madison, United States** * Professor Daniel Sanchez, Massachusetts Institute of Technology, United States** * Professor Aswin Sankaranarayanan, Carnegie Mellon University, United States** * Professor Zhou Yu, Columbia University, United States** * Professor Jun-Yan Zhu, Carnegie Mellon University, United States** * Professor Alan Aspuru-Guzik, University of Toronto * Professor Farrokh Ayazi, Georgia Institute of Technology * Professor Yoshua Bengio, University of Montreal * Professor Mark Brongersma, Stanford University * Professor Siyang Cao, University of Arizona * Professor Virginia de Sa, University of California, San Diego * Professor Jonathan Fan, Stanford University * Professor Kenan Gundogdu, North Carolina State University * Professor Mohit Gupta, University of Wisconsin-Madison * Professor Po-Chun Hsu, Duke University * Professor Yong Jae Lee, University of California, Davis * Professor Jian Lin, University of Missouri * Professor Pedro Lopes, University of Chicago * Professor Xiaoping Qian, University of Wisconsin-Madison * Professor Xiang Ren, University of Southern California * Professor Aswin Sankaranarayanan, Carnegie Mellon University * Professor Akane Sano, Rice University * Professor Randy Sweis, University of Chicago Medicine * Professor Zhou Yu, University of California, Davis * Professor Xinyu Zhang, University of California, San Diego * Professor Jun-Yan Zhu, Carnegie Mellon University * Professor Pulkit Agrawal, Massachusetts Institute of Technology** * Professor David Bishop, Boston University** * Professor Oliver Cossairt, Northwestern University** * Professor Song Han, Massachusetts Institute of Technology** * Professor Xingjie Ni, The Pennsylvania State University** * Professor Dimitris Papailiopoulos, University of Wisconsin-Madison** * Professor Daniel Sanchez, Massachusetts Institute of Technology** * Professor Ted Sargent, University of Toronto** * Professor Pulkit Agrawal, Massachusetts Institute of Technology * Professor David Bishop, Boston University * Professor Oliver Cossairt, Northwestern University * Professor Jonathan Fan, Stanford University * Professor Katerina Fragkiadaki, Carnegie Mellon University * Professor Bolin Liao, University of California Santa Barbara * Professor Patrick Lin, Cal Poly, San Luis Obispo * Professor Wojciech Matusik, Massachusetts Institute of Technology * Professor Florian Metze, Carnegie Mellon University * Professor Xingjie Ni, The Pennsylvania State University * Professor Dimitris Papailiopoulos, University of Wisconsin-Madison * Professor Alexander Rush, Cornell University * Professor Ted Sargent, University of Toronto * Professor Sebastian Scherer, Carnegie Mellon University * Professor Muhammad Shahzad, North Carolina State University * Professor Todd Sulchek, Georgia Institute of Technology * Professor Shimeng Yu, Georgia Institute of Technology * Professor John Zhang, Dartmouth College * Professor Song Han, Massachusetts Institute of Technology** * Professor Aggelos Katsaggelos, Northwestern University** * Professor Daniel Sanchez, Massachusetts Institute of Technology** * Professor Faramarz Fekri, Georgia Institute of Technology * Professor Kristen Grauman, University of Texas at Austin * Professor Song Han, Massachusetts Institute of Technology * Professor Daniel Sanchez, Massachusetts Institute of Technology * Professor Xinyu Zhang, University of California, San Diego * Professor Stefano Ermon, Stanford University** * Professor Aggelos Katsaggelos, Northwestern University** * Professor Shivendra Panwar, New York University** * Professor Dirk Bernhardt-Walther, University of Toronto * Professor Stefano Ermon, Stanford University * Professor Aggelos Katsaggelos, Northwestern University * Professor Scott Kuindersma, Harvard University * Professor Bruno Olshausen, University of California, Berkeley * Professor Shivendra Panwar, New York University * *Awards will be paid in USD or the respective currency depending on the country of the participating university/institution.

* **Renewed Research Collaboration * ***Principal investigator won an award for each of different research topics.

## FACULTY INNOVATION AWARD ### Information Technology Keywords are bulleted under each category title * Affective/Cognitive State Estimation with Context * Emotion Regulation with AI Agent * Motion-robust Remote Vital Sensing * Neural Mechanism of Motion Sickness/Cybersickness #### Audio, Music, Speech, and Language Processing * Multimodal Approaches for Advanced Spatial Audio * Distributed System Infrastructure Framework * Real-time Multi-agent (LLM) Vision System * Software Development Processes in LLM Era * Terascale LLM Software Development/QA * AI-assisted QoE Enhancement for Wi-Fi * AI-based Ray-tracing for Channel Modeling * Deterministic Wi-Fi with Multi-AP Coordination * Non-terrestrial Networks * Use-case-driven Network Optimization via Open RAN * Wi-Fi Systems for Space Environments * Wi-Fi Transformation for AI Traffic * 4D Representations for Physical AI * Co-design of Sensor and Physical AI * Culture-dependent Reasoning in VLMs * Data-efficient RGB-X Learning * Embodied World Model with Multimodal Sensing * Lightweight Performance Predictors for Model Optimization * Sensor-aware Minimal-footprint Neural Imaging * Ultra-low-power/Low-bandwidth Sensing * Vision Model Design with LLM * World Models for Humanoid #### Human Sensing and Interaction * Haptic Sensing and Interaction Technologies * Human-object Interaction, Hand-object Interaction * Interactive Content Creation * Cyber Resilience Technology for Embedded Linux * Embedded Linux Security Assessment Method * Hardening Linux Using External Technologies * OSS License Verification Techniques and Methods * Controllable Character Representation for Cross-domain Animation * Coordinated Motion Generation for Multi-character Interaction * Dexterous Object Interaction for Virtual Character * Monocular 3D Human Pose and Mesh Recovery * Physics-based Character Interaction with Perception * Style-agnostic Representation for Avatar Control * AI-based Indoor-propagation Analysis for RF Sensing * Radar-centric In-cabin Occupant Condition Sensing * Self-supervised Learning for RF Sensing * Vision-language-action for Physical and Virtual Agents * Anti-piracy, Anti-leakage and Its Economics * Efficient Graph Structures in Web3 #### Software Development Technology * Software Architecture Analysis in Large Projects ### Entertainment Technology Keywords are bulleted under each category title #### Audio, Music, Speech, and Language Processing * Automatic Evaluation for Multilingual Expressive TTS * Cross-lingual Expressive Voice Cloning with Natural Prosody * Video-grounded Long-context LLM Agents for Translation * Emotion and Behavior Prediction Using Radar * RF-based Interaction Sensing for Entertainment Experiences * Digital Twins for Expressive Interactive Characters * Sports Technology for Injury Prevention * Sports Technology for Physical/Cognitive Performance #### Visual/Visualization * Automated Content Generation * Digital Human (Capture, Modeling, Rigging, Animation, Rendering, Hair Modeling, Cloth Simulation) * Generative AI for Content Creation * Interactive Material Visualization (Multi-physics) * Neural Rendering/Reconstruction * Physical AI for Content Creation * Volumetric Video Processing Keywords are bulleted under each category title * High-efficiency Red Micro LED * MicroLED-based Optical Interconnect * Monolithic RGB/Single-chip Architecture * Optical Extraction/Resonant Structure #### Optical Metasurface Design Theory/Framework * Design Enhancement by Physics-informed AI/DL * Image Quality Centric Design Optimization * Metasurface Spaceplate for Compact Lens System * Energy-efficient RF Transceiver/Receiver Design * Joint Optimization of Radar-waveform and Signal-processing #### Spatial Light Modulator * 2D Array Tunable Metasurface Light Modulator * *Awards will be paid in USD or the respective currency depending on the country of the participating university/institution.

## FOCUSED RESEARCH AWARD Solid research is the underlying driving force to crystallize fearless creativity and innovation. While we are committed to run in-house research and engineering, we are also excited to collaborate with academic partners to facilitate exploration of new and promising research.

The Sony Focused Research Award provides an opportunity for university faculty, research institutes, and Sony to conduct this type of collaborative, focused research. The award provides up to $150K USD* in funds, and may be renewed for subsequent year(s). A list of candidate research topics appears below.

Please select the Focused Research Theme for which your submission is written. ### Internal Mechanisms of Multimodal Generative Models for Content Creation This theme focuses on the internal mechanisms of LLMs, MLLMs, speech/audio models, music models, and video generation systems, motivated by their growing role in multimodal content creation.

As these models increasingly shape how text, images, video, speech, sound, and music are generated, edited, transformed, and interpreted, it becomes important to understand not only their outputs, but the internal processes by which they encode sources, bind modalities, reuse learned patterns, represent style and identity, preserve or distort factual signals, and respond to causal interventions.

Sony is seeking research that moves beyond artifact-level analysis toward mechanistic, causal, and representation-level accounts of generative behavior, with particular interest in methods that reveal how internal states, circuits, latent variables, modality-fusion pathways, attention, and generation dynamics give rise to multimodal content.

In addition, we are particularly interested in technologies that enable the externalization of knowledge, making it easier to support flexible opt-in and opt-out mechanisms for data usage, as well as more explicit and transparent attribution of sources and contributions.

Novel technologies including but not limited to: * Mechanistic interpretability of multimodal generation: Studies that identify circuits, features, latent variables, attention pathways, denoising dynamics, or other internal mechanisms underlying generative behavior.

* Causal attribution inside generative models: Research that traces how inputs, context, retrieval, training data, model components, or generation steps causally influence intermediate representations and final outputs.

* Internal representations of source, authorship, and provenance: Work on how models encode the origin, transformation history, epistemic status, or source dependence of information across prompts, context, retrieval, and generation.

* Knowledge externalization and controllable data usage: Technologies that enable the externalization of knowledge, making it easier to support flexible opt-in and opt-out mechanisms for data usage, and facilitating more explicit, transparent, and controllable attribution of sources and contributions.

* Representations of style, identity, and modality-specific structure: Research on how models encode writing style, visual identity, speaker identity, prosody, timbre, melody, rhythm, genre, motion, sound events, and other structured signals.

* Cross-modal binding, grounding, and inconsistency: Work on how models integrate, route, ignore, or misalign information across language, vision, audio, video, speech, music, time, metadata, and external context.

* Intervention, editing, and controllability of internal mechanisms: Studies that use activation steering, circuit editing, representation editing, latent-space control, or causal interventions to modify, verify, or explain model behavior.

### Anime-Style Expression Exaggeration – Learning Artistic Deformation Priors Anime and stylized characters rely on artistic exaggeration - eyes widening dramatically in surprise, mouths opening unrealistically large in shock, tears, sweat-drops, and other non-physical visual conventions that convey emotion.

These exaggerations are not derivable from photoreal performance capture and are typically authored manually by skilled animators. As anime and stylized content grows central to Sony's IP, automating or assisting this exaggeration process is a significant unmet need.

Sony seeks proposals on learning artistic deformation priors directly from anime data, keyframes, animation cels, motion sheets, and applying them to drive expressive, style-faithful animation of stylized characters from real performance input.

* Learning artistic exaggeration priors from 2D anime / stylized animation datasets * Mapping subtle photoreal performance to exaggerated stylized output * Per-style or per-character exaggeration models (different anime styles have different conventions) * Integration with 2D and 3D anime character rigs * Optional: handling of non-physical visual elements (sweat drops, blush marks, expression symbols) * Approaches: style-transfer with deformation priors, diffusion on rig parameters, learned exaggeration operators * Evaluation: artistic faithfulness, style consistency, animator usability studies ### Knowledge Representation and Reasoning for Graph-Based Modeling This research direction investigates how ontologies and knowledge representation methods can support more explainable, reusable, and adaptable graph-based modeling of interaction data.

The focus is on user profile generation for personalized recommendation, micro-region segmentation, and explainability, using formal knowledge structures to enrich user, content, and business representations, infer hidden properties and relationships, represent subjective signals such as likes, dislikes, preferences, and engagement intent, and expose reasoning paths across different domains and business scenarios.

* Develop ontology-based methods to enrich graph-based representations of users, content, interactions, and business concepts with inferred properties, semantic relationships, and contextual knowledge. * Investigate how subjective user signals, such as likes, dislikes, preferences, satisfaction, engagement quality, and intent, can be formally represented as part of user profiles and reasoned over.

* Explore how ontologies can support graph enhancement by identifying hidden links, suggesting new relationships, validating entity types, and improving the semantic structure of interaction graphs. * Study how ontology-driven reasoning can improve explainability by connecting recommendations, user segments, and personalization decisions to explicit concepts, rules, assumptions, and reasoning paths.

* Investigate mechanisms for validating knowledge consistency, detecting hidden or conflicting information, and maintaining reliable graph-based knowledge structures over time. * Explore cross-domain and cross-business mappings through shared or aligned ontologies, enabling knowledge reuse between different recommendation, personalization, and user engagement scenarios.

* Evaluate how knowledge representation and reasoning can improve transparency, debugging, maintainability, and adaptability in practical graph-based personalization workflows. ### Psychology-Informed User Modeling and Intervention Design for Content Streaming Experiences In content streaming services, understanding users is fundamental to evaluating content, designing experiences, and informing creative and marketing decisions.

Today this understanding relies largely on demographics and behavioral metrics such as watch/listen time, completion, and retention.

These signals capture what users do, but not why they engage, what kind of value an experience delivers, or what would actually move them -- two users consuming the same content may seek entirely different things, from sensory pleasure to meaning, mastery, or self-expression, and may respond to the same intervention in opposite ways.

AI is expected to bridge this gap by inferring richer user understanding from data and connecting it to action. However, current approaches face the following challenges: * Beyond Behavioral Signals: It is difficult to infer users' underlying values, needs, and experiential orientation (e.g., hedonic vs. eudaimonic) from behavioral logs alone.

* Interpretable, Theory-Grounded Representations: User models are often black-box embeddings that cannot be communicated to non-technical stakeholders such as creators and marketers.

* From Understanding to Intervention: Methods are optimized for recommendation accuracy or descriptive prediction, rather than identifying what intervention (e.g., marketing actions, messaging, content offers) would lead a given user to discover content, change behavior, or broaden their tastes.

To address these challenges, Sony is seeking the development of innovative methods such as, but not limited to the following: Psychology-Informed User and Content Modeling * Infer latent user constructs -- values, needs, and experiential orientation -- from behavioral and experiential data, grounded in established theory, * Characterize content along the same experiential dimensions, capturing what kind of experience a title delivers and which constituent elements drive value for different users, * Combine first-party behavioral data with publicly available experiential signals (e.g., reviews, social text), without relying on intrusive personal profiling, Insight Extraction and Intervention Design * Generate interpretable, actionable insight that draws on psychological and sociological knowledge (e.g., theories of values, motivation, taste, and cultural consumption) to support audience understanding, content evaluation, and creative/marketing strategy beyond recommendation accuracy, * Identify which user attributes govern responsiveness to intervention, and what actions (e.g., messaging, creative, content offers, timing) most effectively spark interest, change behavior, or shift preferences for different users -- distinguishing short-term engagement from longer-term taste formation, * Establish evaluation methodology beyond offline accuracy metrics, such as user-centric and intervention-effect (uplift) evaluation.

### Post-Training of SpeechLMs for Anime Speech LMs are gaining attention for their utility in generating and analyzing voice assets for entertainment content. Sony produces and distributes a vast catalog of Anime, much of which remains un-localized for global audiences, and character-based voice interaction offers a promising avenue to deepen fan engagement with our IPs.

However, adapting speech LMs to capture anime-specific stylistic, emotional, and cultural nuances, while remaining controllable, remains an open challenge. Sony seeks proposals addressing these challenges to enhance fan engagement with entertainment IPs.

Novel technologies for adapting speech LMs toward specific domains using post-training, including but not limited to: * To improve translation that is aware of cultural context; * To localize the content at a production quality; * To realize a controllable conversational system; or * Automated data annotation pipeline for post-training ### Innovative Visual Technology Powered by AI Recent advances in machine learning have created a paradigm shift across a wide range of applications.

In particular, foundation models and generative AI have significantly expanded the capabilities of image and video processing, enabling not only recognition and reconstruction, but also high-quality content generation, understanding, and interaction.

Sony is seeking innovative research in image and video technologies based on machine learning to significantly advance existing techniques and applications across 2D as well as 3D/4D domains. This includes, but is not limited to, content generation, multimodal understanding, spatial-temporal modeling, and efficient representation.

Our goal is to create new value for creators by enabling novel forms of expression, improving creative workflows, and unlocking new content experiences through advanced visual technologies.

Topics of interest include: * Generative AI such as image to photo-realistic video, video to video, style transfer, modal transfer based on a new approach, e.g. photo-realistic image generation using neural rendering or novel generative model with high controllability, * Multimodal content generation based on vision language model(VLM), * Physical AI(vision language action model: VLA) that connects the virtual and real world, * Novel 3D/4D model generation/reconstruction, e.g. NeRF, 3D/4D gaussian splatting, world model generation, * Perceptual metrics for predicting photo-realistic image quality or 3D image quality based on VLM, and * Explainable and controllable AI for visual content processing and generation.

### AI-Based Digital Human Content Creation for Sports Science and Sports Entertainment Sony is seeking innovative research on AI and machine learning-based techniques for data-driven digital human 3D model creation and animation in the field of sports science and sports entertainment.

Relevant sports science application areas include biomechanics, physiology, coaching and training, performance analysis, and injury prevention and monitoring.

The objective is to significantly improve the quality, accuracy, and efficiency of existing sports monitoring and diagnostic workflows, while also enabling the development of new tools that help understand, optimize, and enhance athletes' performance, physical health, and well-being. Relevant sports entertainment application areas include immersive 3D/4D visualization techniques for players’ replay and fan engagement.

The current state-of-the-art approach (3D/4D Gaussian splatting) takes hours to process. The objective is to develop novel photo-realistic content creation techniques based on AI-generated digital human assets in real-time or within a few minutes.

Topics of interest include, but are not limited to: * High-fidelity biomechanical measurement, analysis, and diagnostics for improving athletic performance and health using digital human CG models and animations generated from 2D images and videos.

* High-fidelity immersive sports visualization that delivers emotional engagement and novel experiences beyond conventional 2D images and videos, enabling deeper fan engagement and richer connections with athletes and the sporting events in which they compete.

### Provenance Proofs for AI-Generated Derivative Content * Provenance Representation Models * Define a standardized provenance graph that captures relationships between original content, AI-generated outputs, and subsequent derivatives. * Support multimodal content including text, images, audio, video, and 3D assets.

* Cryptographic Provenance Infrastructure * Develop cryptographic mechanisms (hash chaining, digital signatures, Merkle trees, zero-knowledge proofs) to establish tamper-evident provenance records. * Enable verification without exposing proprietary content. * Derivative Content Attribution * Design algorithms to identify and