The 10th International Workshop on Health Intelligence (W3PHIAI-26) will celebrate a decade of convening researchers, clinicians, computer scientists, public health agencies, NGOs, and industry leaders at the intersection of AI and health.

This year’s theme, Foundation Models and Agentic AI in Healthcare and Medicine, will explore how large-scale pretrained models and autonomous systems are transforming clinical workflows, patient management, health system operations, and public health surveillance. The workshop aims to foster discussion on developing robust Foundation Models and AI agents that integrate multimodal health data, enhance patient engagement, support clinicians, and ensure equitable, safe, and trustworthy AI adoption in real-world settings. Building on nine highly successful W3PHIAI events, we anticipate two vibrant days of keynotes, panels, paper presentations, posters, demos, and a hackathon.

Topics of Interest

We invite contributions in (but not limited to):

Knowledge Representation and Reasoning
Integrated and Interoperable Health Systems
Clinical Deployment and Integration
Patient-Focused Workflows
Shared Decision Making
Epidemic Intelligence and Early Outbreak Detection
Predictive Modeling and Clinical Decision Support
Biomed Ontologies, Terminologies, and Standards
Bayesian Networks and Reasoning under Uncertainty
Temporal and Spatial Representation and Reasoning
Case-based Reasoning in Healthcare
Crowdsourcing and Collective Intelligence
Risk Assessment, Trust, Ethics, Privacy, Evaluations, and Security
Computational Behavioral/Cognitive Modeling
Intervention Design, Modeling, and Evaluation
Multimodal Learning and Hybrid Methods
Generative AI and Foundation Models
Agentic AI
Conversational Agents

Workshop Format

This is a two-day workshop featuring keynote and invited talks, panels, oral and poster presentations, and demos. A special track will highlight this year’s theme of Foundation Models and agentic AI.

Attendance

Open to researchers, practitioners, policymakers, and students in AI and health. Attendance is first-come, first-served until capacity is reached.

Submission Guidelines

Full papers (up to 8 pages)
Short papers (up to 4 pages)
Posters/demos (up to 2 pages)

All submissions should follow the AAAI format (single-blind review) and be submitted via EasyChair: https://easychair.org/conferences?conf=w3phiai26.

Selected papers will be invited for oral, poster, and spotlight presentations. Proceedings will be published in a Springer book collection, with opportunities for journal special issues.

Important Dates

October 20, 2025: Submissions due
November 5, 2025: Notification of acceptance
November 16, 2025: AAAI-26 Early Registration Deadline
December 15, 2025: Final Camera-Ready Version
January 26-27, 2026: The Workshop Program

Organizing Committee

Martin Michalowski, PhD, FAMIA, FIAHSI, University of Minnesota (martinm@umn.edu)
Arash Shaban-Nejad, PhD, MPH, University of Tennessee Health Science Center (ashabann@uthsc.edu)
Simone Bianco, PhD, Altos Labs (sbianco@altoslabs.com)
Creighton Heaukulani, PhD, Ministry of Health Office for Healthcare Transformation, Singapore (creighton.heaukulani@moht.com.sg)
Marija Stanojevic, PhD, Ellipsis Health (mstanojevic118@gmail.com)
Robert Morris, PhD, Ministry of Health Office for Healthcare Transformation, Singapore (robert.morris@moht.com.sg)

Workshop Website:

http://w3phiai2026.w3phi.com

W2: Agentic AI in Financial Services

Financial services play a crucial role in everyday life, requiring expert-level support to meet highly personalized user needs across domains like banking, insurance, and taxation. The emergence of foundation models, especially large language models (LLMs), has introduced new capabilities in communication, reasoning, and personalization that align well with financial decision-making processes. Recently, agentic AI has extended these models by enabling them to autonomously plan, reason, and act across multi-step tasks, making them highly suitable for complex use cases such as financial advising and compliance.

Co-located with AAAI’26, this workshop aims to bring together researchers and practitioners to explore the latest advances in agentic AI for a wide range of financial services, fostering discussions and new ideas on design, deployment, ethics, and real-world impact.

Topics of Interest

This workshop encourages submissions of innovative solutions for a broad range of problems in finance. Topics of interest include but are not limited to the following:

Research, implementation, or deployment of agentic AI in financial decision-making;
Multi-agent systems for market/user simulation and trading;
Foundation models for banking, trading, and other financial services;
Explainability and trust in financial AI agents;
Agentic AI for regulatory compliance and auditing;
Conversational financial agents and advisors;
Autonomous document processing and financial knowledge extraction;
Risk management with agentic AI, e.g., fraud detection and credit assessment;
Agentic workflows/architectures in financing platforms;
Ethical and policy aspects of agentic AI in finance.

Workshop Format

The workshop will be a half-day event, featuring keynote speeches, paper presentations, and poster sessions.

Attendance

The workshop welcomes AAAI conference attendees with an interest in LLM, financial technology, information retrieval, and recommender systems. There are no restrictions on attendance, and the maximum number of attendees will be determined by the room capacity.

Submission Guidelines

Submission Requirements

Long Papers: 5-7 pages of main content, plus unlimited pages for references followed by appendices (if any).

Short Papers, Extended Abstract, and Enlightening Talks: No more than 4 pages of main content, plus unlimited pages for references followed by appendices (if any).

All submissions should adhere to the AAAI’26 formatting guidelines and will undergo a peer review process.

Submission Site Information

https://openreview.net/group?id=AAAI.org/2026/Workshop/AI-4-Finance

Organizing Committee

Tong Chen, The University of Queensland (Main Contact, tong.chen@uq.edu.au)
Hongxu Chen, Commonwealth Bank of Australia
Joel Mackenzie, The University of Queensland
Fengbin Zhu, National University of Singapore
Luiz Pizzato, Commonwealth Bank of Australia
Ritchie Ng, Commonwealth Bank of Australia
Anna Leontjeva, Commonwealth Bank of Australia

Workshop Website:

For more details, visit https://ai-4-finance.pages.dev

W3: AI for Healthy Aging and Longevity (AIAA2026)

The number of people in the world aged 65 or over will more than double to reach 1.5 billion by 2050, according to the United Nations. The rapid aging of the global population has brought unprecedented economic and cultural challenges to individuals, families, society, as well as the international community. To alleviate the challenges of an aging society, the elderly population requires support in active lifestyles, balanced nutrition, hospital care at home, and healthcare services. Furthermore, healthy and thriving elderly can contribute significantly to society.

This workshop focuses on the intersection of Artificial Intelligence (AI) for health aging and longevity research, aiming to address the global challenges brought by rapid population aging. The main objective is to explore how AI technologies can support active lifestyles, disease management, healthcare affordability, and longevity, enabling older people to lead active, independent, and dignified lives.

Topics of Interest

We invite submissions of research/survey/application/demo papers related to the AI applications/theories in aging, including but not limited to the following:

AI and aging
AI and disease prevention
AI for Longevity
AI for healthcare
Cognitive analytics
Preventive intervention
Rehabilitation
Elderly user behaviour analysis and modelling
Serious games
Virtual companions
Human-agent interaction design
Early identification of frailty
Frailty diagnostic support
LLMs in healthcare
Age in place
Hospital care at home
Mobile inpatient care at home
Agentic AI for healthy aging and longevity

Workshop Format

The workshop is a half-day event (4 hours, 2:00 PM – 6:00 PM, Jan 26, 2026).
It will feature invited talks, research paper presentations, and panel discussions.

Attendance

Open to researchers, practitioners, students, and industry stakeholders. Attendance is not restricted, but space may be limited depending on venue capacity.

Submission Guidelines

Submissions may include:

Full research papers (limited to 6 pages, including references)
Short papers or extended abstracts (limited to 500 words)

All submissions should follow the in free format or the International Journal of Crowd Science format.

Submission Site Information
Submissions should be made via CMT (https://cmt3.research.microsoft.com/AIAA2026).

Organizing Committee

Workshop Chair:

Prof. Chunyan Miao, Nanyang Technological University, Singapore, (ASCYMiao@ntu.edu.sg)

Organizing Committee

Chunyan Miao, Nanyang Technological University, Singapore (ASCYMiao@ntu.edu.sg)
Cyril Leung, The University of British Columbia, Canada (cleung@ece.ubc.ca)
JingJih Chin, Tan Tock Seng Hospital, Singapore (jing_jih_chin@ttsh.com.sg)
Xuejiao Zhao, Nanyang Technological University, Singapore (xjzhao@ntu.edu.sg)

Program Committee

Takayuki Ito, Kyoto University, Japan (ito@i.kyoto-u.ac.jp)
Cuntai Guan, Nanyang Technological University, Singapore (ctguan@ntu.edu.sg)
Lizhen Cui, Shandong University, China (clz@sdu.edu.cn)
Lim Wee Shiong, Tan Tock Seng Hospital, Singapore (weeshionglim@ttsh.com.sg)
Martin J. Mckeown, The University of British Columbia, Canada (martin.mckeown@ubc.ca)
Yiqiang Chen, Chinese Academy of Sciences, China (yqchen@ict.ac.cn)
Toru Ishida, Kyoto University, Japan (toru_ishida@comp.hkbu.edu.hk)
Zhiqi Shen, Nanyang Technological University, Singapore (zqshen@ntu.edu.sg)
Hong Xu, Nanyang Technological University, Singapore (xuhong@ntu.edu.sg)
Huanhuan Chen, University of Science and Technology of China (hchen@ustc.edu.cn)
Xiaoxiao Li, The University of British Columbia, Canada (xiaoxiao.li@ece.ubc.ca)
Zhiwei Zeng, Nanyang Technological University, Singapore (zhiwei.zeng@ntu.edu.sg)
Haocong Rao, Nanyang Technological University, Singapore (haocong001@e.ntu.edu.sg)
Xiao Yang, Nanyang Technological University, Singapore (XIAO009@e.ntu.edu.sg)

Workshop Website:

https://aaai-aiaa-2026.com/

W4: AI in Agriculture (Agri AI)

Agriculture is undergoing a rapid transformation driven by digital technologies, including IoT sensors, drones, satellite imagery, and mobile platforms, which generate vast streams of heterogeneous, temporally rich data. These advancements offer unprecedented opportunities for AI to enable smarter, more resilient, and efficient agricultural practices. As climate change increases variability in crop yields, pest outbreaks, and resource availability, traditional agricultural methods are proving inadequate in addressing these challenges. Although AI has started to tackle these issues through applications like crop forecasting, disease detection, and irrigation optimization, many existing solutions remain narrow in focus, difficult to generalize, and often lack scalability and adaptability. Furthermore, important human-centered concerns, such as transparency, fairness, and participatory system design, are often underexplored. The First International Workshop on AI in Agriculture (Agri AI) aims to address these gaps by bringing together researchers and practitioners to advance robust, responsible, and scalable AI methods tailored to real-world agricultural systems.

Topics of Interest

This workshop will cover the following research themes, including but not limited to the topics listed below.

Data Foundations

Multi-modal heterogeneous agriculture data collection and fusion (satellites, drones, IoT sensors, camera)
Large-scale, open source, high-quality benchmark datasets for AI in agriculture
Synthetic data generation and simulators to address uncertainty and data/label scarcity

Decision Intelligence and Scalable AI Models

AI-powered decision support systems for precision farming and resource optimization
Generative AI, foundation models, and transfer learning for agriculture
Edge AI, federated and distributed learning for agriculture

Applications and Real-World Deployments

AI-driven plant disease and insect detection, yield forecasting, etc.
Longitudinal study, field validation, and performance benchmarking for real-world case studies
Security, privacy and trustworthiness of AI in agriculture

Workshop Format

The workshop will follow a half-day format, focusing on keynote and invited talks, oral/poster presentations of the accepted papers, and a closing panel discussion.

Attendance

Attendance is open to all AAAI attendees. At least one author of each accepted submission must be present at the workshop.

Submission Guidelines

We invite research papers presenting original results, including deployment experiences and case studies, that have not been published previously and are not under concurrent review elsewhere. Papers can be up to 6 pages, including figures, tables, and references. All submissions must follow the AAAI 2026 submission format and be submitted electronically. The workshop will follow a single-blind review process; therefore, authors should include their names, affiliations, and contact details in the paper.

Submission Site Information: https://openreview.net/group?id=AAAI.org/2026/Workshop/AgriAI

Organizing Committee

Workshop Chair:

Sajal K. Das, Missouri University of Science & Technology, USA, sdas@mst.edu
Pandarasamy Arjunan, Indian Institute of Science, Bangalore, India, samy@iisc.ac.in

Workshop Committee:

Sajal K. Das, Missouri University of Science & Technology, USA, sdas@mst.edu
Pandarasamy Arjunan, Indian Institute of Science, Bangalore, India, samy@iisc.ac.in
Soumik Sarkar, Iowa State University, USA, oumiks@iastate.edu

Workshop Website:

https://agriai-workshop.github.io/aaai2026

W5: AI to Accelerate Science and Engineering (AI2ASE)

Scientists and engineers in diverse application domains are increasingly relying on using computational and artificial intelligence (AI) tools to accelerate scientific discovery and engineering design.
AI, machine learning, and reasoning algorithms are useful in building models and decision-making towards this goal.
We have already seen several success stories of AI in applications such as materials discovery, ecology, wildlife conservation, and molecule design optimization.

This workshop aims to bring together researchers from AI and diverse science/engineering communities to achieve the following goals:

Identify and understand the challenges in applying AI to specific science and engineering problems.
Develop, adapt, and refine AI tools for novel problem settings and challenges.
Community-building and education to encourage collaboration between AI researchers and domain area experts.

Workshop Format

The workshop schedule will include a series of invited talks, panel discussion, breakout session and poster session.

Invited Speakers:

This year’s theme is AI for agricultural and forestry sciences. Our invited speakers, who are working on important problems at the intersection of AI and domains, include:

Prof. Shashi Shekhar, AI-LEAF Institute Director / University of Minnesota
Prof. Bryan Kian Hsiang Low, National University of Singapore

Length of the workshop: Half day

Attendance

We invite everyone attending AAAI conference to join for the workshop. We believe this is an exciting problem space where the most promising technology of our generation can help address most challenging problems of our society by accelerating scientific discovery and engineering design.

Submission Guidelines

Submission Requirements:

We welcome submissions of long (max. 8 pages), short (max. 4 pages), and position (max. 4 pages) papers describing research at the intersection of AI and science/engineering domains including chemistry, physics, power systems, materials, catalysis, health sciences, computing systems design and optimization, epidemiology, agriculture, forestry, transportation, earth and environmental sciences, genomics and bioinformatics, civil and mechanical engineering etc.

Submissions must be formatted in the AAAI submission format. All submissions should be done electronically via CMT.

Submission Site: https://cmt3.research.microsoft.com/AItoASE2026

Organizing Committee

Workshop Chair: Aryan Deshwal, adeshwal@umn.edu

Workshop Organizing Committee:

Aryan Deshwal, University of Minnesota, adeshwal@umn.edu
Jana Doppa, Washington State University, jana.doppa@wsu.edu
Vipin Kumar, University of Minnesota, kumar001@umn.edu
Carla Gomes, Cornell University, gomes@cs.cornell.edu
Azza Fadhel, Washington State University, azza.fadhel@wsu.edu

Workshop Website:

https://ai-2-ase.github.io

W6: Addressing Challenges and Opportunities in Human-Centric Manufacturing

Human-centric manufacturing envisions a reciprocal partnership between people and machines. However, realizing this potential presents significant challenges in the development of advanced technologies. Notably, differences between human and machine intelligence emphasizes the importance of gaining a deeper understanding of the role of AI in facilitating interactions within the workplace. Subsequently, this one-day workshop aims to investigate how AI can support intuitive collaboration and foster creativity in manufacturing. By bringing together individuals from diverse backgrounds, a focus is to understand how co-developed approaches can drive innovation and generate shared ideas within evolving manufacturing ecosystems. Specifically, to gather insights into existing and emerging trends, review and correlate research and development works, and formulate actionable items into tangible research directions.

Topics of Interest

Related to human-centric manufacturing, topics include, but are not limited to the following:

Situation aware and assistance systems
Detection, tracking and monitoring
Planning for human uncertainty and error
Enhancing and amplifying human capabilities
Human-robot collaboration
Human-in-loop agentic AI
Digital twins and simulation
Model interpretability and bias
Explainable AI
Evaluation measures and empirical benchmarks
Concepts and frameworks
Privacy protection and ethical issues
Knowledge transfer across domains
Industrial use cases

Workshop Format

The workshop will include morning, and afternoon invited talks. Accepted papers will be structured as oral presentations, which we are looking to separate by related themes. To conclude the workshop, a panel discussion will aim to summarize key insights, while facilitating further discussion and knowledge sharing.

Attendance

The workshop is open to those interested to attend (with presently no known number restrictions). Across different backgrounds, we welcome contributions from educators, researchers, and practitioners to promote discussions and collaboration in the development of human-centric solutions for manufacturing.

Submission Guidelines

We invite full technical papers (6 to 8 pages), and short/work-in-progress papers (2 to 4 pages), excluding references and supplementary material.

Papers submissions are required to be anonymized for single blind review. Please follow the AAAI paper format guidelines: https://aaai.org/conference/aaai/aaai-26/submission-instructions/

Submission Information

Submissions should be sent to: https://openreview.net/group?id=AAAI.org/2026/Workshop/HCM

Important Dates

Workshop paper submissions due: Friday, October 24, 2025
Notifications sent to authors: Friday, November 7, 2025
Workshop date: Monday, January 26 or Tuesday, January 27, 2026 (TBC)

Organizing Committee

Mark Rice (mdrice@i2r.a-star.edu.sg), Institute for Infocomm Research, A*STAR, Singapore
Shijian Lu (shijian.lu@ntu.edu.sg), Nanyang Technological University, Singapore
Gu Ying (guy@i2r.a-star.edu.sg), Institute for Infocomm Research, A*STAR, Singapore
Lai Xing Ng (ng_lai_xing@i2r.a-star.edu.sg), Institute for Infocomm Research, A*STAR, Singapore

Enquiries on the workshop should be sent to: mdrice@i2r.a-star.edu.sg

Workshop Website:

https://sites.google.com/view/hcm-aaai26

W7: Advancing Artificial Intelligence through Theory of Mind (ToM4AI)

Theory of Mind, the ability to reason about and attribute mental states, such as beliefs, intentions, desires, and emotions – to oneself and others, is essential for all social inference tasks. As artificial intelligence (AI) systems evolve towards greater interactivity, ToM principles enable better interpretations and responses of human actions and intentions to provide the equivalent computational efficiency humans enjoy.

This workshop will convene researchers from diverse fields, ranging from psychology, philosophy, cognitive science, neuroscience, robotics, and AI, to explore the implications of ToM in developing advanced AI systems. We aim to bridge the gap between theory-driven cognitive science and practical AI applications, fostering a multidisciplinary dialogue on the role of ToM in AI. The outcomes of this workshop will contribute to the theoretical understanding of ToM in AI and inspire new research directions, collaborations, and an interdisciplinary community focused on this topic.

Topics of Interest

Topics include, but are not limited to:

Developmental ToM: Insights for AI
The Role of ToM in Human-AI Interaction
Computational ToM
ToM in Large Language Models: Emulation, Emergence, or Anthropomorphic Illusion?
Plan and goal recognition
ToM in human-robot interactions
Epistemic reasoning and planning
BDI and adaptive cognitive architectures
Multi-agent communication and meta-cognition
Knowledge representation and meta-reasoning
ToM evaluation and benchmarks
Foundational models of ToM
Philosophical aspects of Artificial Mind-reading

Workshop Format

This is a 1 day event, including 3 keynote talks (speakers listed below), paper presentation session and poster sessions and a communal hackathon.

Prof. Geoff Bird (Oxford University)
Prof. Sarit Kraus (Bar-Ilan University)
Prof. Martin Sap (Carnegie Mellon University)

Attendance

We invite participation from researchers interested in directly addressing Theory of Mind in AI. We are also expecting submissions to ToM4AI and related topics relevant to designing, developing, demonstrating, or evaluating ToM-based AI agents.

Submission Requirements

Papers should be submitted via OpenReview

We will accept submissions in the form of extended abstracts (up to 2 pages, references excluded, appendix allowed) on the broader ToM4AI spectrum. Papers must conform to the AAAI-26 submission instructions found here (zip file available on the workshop site). Reviews are double-blind.

Organizing Committee

Nitay Alon (HUJI, Max-Planck Cybernetics) – Nitay.alon@mail.huji.ac.il
Joseph M. Barnby (KCL) – joseph.barnby@kcl.ac.uk
Reuth Mirsky (Tufts university)
Stefan Sarkadi(KCL) – stefan.sarkadi@kcl.ac.uk

Workshop Website:

More information is available on our website

W8: Agentic AI Benchmarks and Applications for Enterprise Tasks

The primary goal of this workshop is to foster discussions and collaborations to build robust, efficient, and trustworthy Agentic AI technologies for complex and dynamic enterprise business operations. It aims to bridge the gap between cutting-edge Agentic AI research and the practical demands of enterprise deployment and rigorous evaluation.

Topics of Interest

The workshop will address the following specific issues:

Benchmarking and Evaluation: Addressing the urgent need for robust benchmarks, datasets, and metrics to reliably evaluate Agentic AI systems for enterprise-level performance, safety, and reliability, including the challenges of creating realistic and representative enterprise task environments.
Application of Agentic AI in Enterprise Settings: Exploring how Agentic AI can perform complex tasks such as understanding on-site operations, planning, observation, reflection, and system management within enterprise contexts.
Human-Agent Interaction in Enterprise Workflows: Development and deployment of intelligent assistants that augment human capabilities in business operations.
Multimodal Reasoning for Enterprise Tasks: Integrating multimodal LLMs to handle diverse physical data (e.g., visual, textual, auditory) for robust decision-making and task execution in enterprises.
Task Planning and Orchestration in Enterprise Environment: Strategies for integrating multiple agents and tools to achieve complex, multi-step enterprise goals.
Others: We are also seeking a wide range of content related to Agentic AI technology.

Workshop Format

The workshop is designed to be highly interactive, fostering significant discussion and collaboration. It will be organized as a one-day event, allowing for in-depth exploration of the topic without overwhelming participants. The format will include:

Invited Talks: We plan for some invited talks from leading experts in enterprise applications and benchmarks for Agentic AI or multimodal LLMs, such as knowledge work, field work, and human agent interaction. These talks will cover foundational research, case studies, and future directions, delivered by speakers from diverse backgrounds (academia and industry).
Paper Presentations: Some portion of the workshop will be dedicated to presenting accepted research papers. We will prioritize longer presentation slots for oral presentations and encourage poster sessions for all accepted papers to maximize interaction.
Panel Discussions: Some panel discussions will be organized, focusing on key challenges and future directions. These panels will involve invited speakers, paper authors, and other prominent researchers, fostering dynamic debate and community engagement.

Submission Guidelines

All contributions must be original, unpublished and should not be under consideration by other conferences or journals. Submissions will undergo a peer review process using a single-blind system. The evaluation criteria include relevance to the workshop, novelty, technical contribution, impact significance, clarity, and reproducibility. To ensure consistency, all submissions must adhere to the AAAI-26 formatting guidelines and utilize the corresponding LaTeX style files.

Two types of submissions are accepted:

Full research papers, which should not exceed 8 pages (including references)
Short/poster papers, which should not exceed 4 pages (including references)

Accepted papers require in-person presentation by at least one author. If you have any queries regarding the submission process, please contact us at aaai26ws.aaba4et@gmail.com.

Submission Site: https://openreview.net/group?id=AAAI.org/2026/Workshop/AABA4ET

Organizing Committee

Graham Neubig: Associate Professor, Language Technologies Institute, Carnegie Mellon University (gneubig@cs.cmu.edu)
Yonatan Bisk: Assistant Professor, Language Technologies Institute, Carnegie Mellon University (ybisk@cs.cmu.edu)
Hideo Saito: Professor, Department of Information and Computer Science, Keio University (hs@keio.jp)
Atsunori Moteki: Principal Researcher, Artificial Intelligence Laboratory, Fujitsu Limited (moteki.atsunori@fujitsu.com)

The program committee, comprising leading researchers and practitioners with deep expertise in Agentic AI, multimodal systems, natural language processing, and real-world enterprise applications, will ensure a balanced perspective on theoretical advancements and practical challenges.

Workshop Website:

https://sites.google.com/view/aaba4et

W9: AI for CyberSecurity (AICS) 2026

The workshop will focus on the application of artificial intelligence (AI) to problems in cyber security. While AI has shown tremendous promise in enhancing human decision-making in cyber security and even automating critical security functions, the security of these AI-enabled systems themselves remains a vulnerable frontier. The workshop will address technologies and their applications in security, such as, machine learning, game theory, natural language processing, knowledge representation, automated and assistive reasoning and human machine interactions.

This year, we will welcome a special focus on the agentic tool-use of cybersecurity systems and tools. Security Operation Centers (SOCs) and malware analysts are inundated with tools, alerts, and systems that require years of experience, large amounts of contextual information, and were not previously designed to interoperate together. These challenges make Cybersecurity a particularly fruitful area for research with real-world impact.

Topics of Interest

Topics of interest include, but are not limited to:

Security and Robustness of AI-enabled Systems
AI in Cyber Security
Game Theoretic and Multi-agent Security Approaches
Human Behavioral Modeling
Ethics, Privacy, and Governance
(AI mentioned above encompasses learning, planning, optimization, generative AI, etc.)

Workshop Format

The workshop will have two invited talks. We will host a series of short (15-20 minute) presentations of the papers accepted for this workshop, and a poster or panel session depending on the number of quality submissions.

The AICS workshop will be a one-day meeting, from roughly 9am to 5pm.

Attendance

The organizers do not impose any criteria to attend, other than what AAAI registration imposes. The maximum number of attendees is to be determined by the room size.

Submission Guidelines

Submission requirements: We accept only full-length papers (min of 6 pages, up to overall 8 pages in the AAAI26 format). Submissions are not anonymized.

Submission Site Information: http://aics.site/AICS2026/cfp.html

Workshop Chairs

Edward Raff edward.raff@crowdstrike.com and raff.edward@umbc.edu
Ahmad Ridley adridle@uwe.nsa.gov
Dennis Ross Dennis.Ross@ll.mit.edu
Sagar Samtani ssamtani@iu.edu
Ankit Shah ankit@iu.edu
Arunesh Sinha arunesh.sinha@rutgers.edu
Allan Wollaber Allan.Wollaber@ll.mit.edu
Wai Tuck Wong wt.wong.2022@phdcs.smu.edu.sg

Workshop Website:

http://aics.site/AICS2026/

W10: AI for Scientific Research

The workshop aims to foster collaboration and stimulate innovation towards the development of next-generation AI research assistants that are reliable, transparent, and seamlessly integrated into the fabric of scientific discovery. Recent advancements in generative AI and agentic systems have unlocked the potential to automate and accelerate every stage of research, from hypothesis generation to paper writing. The rapid advancement of AI-assisted scientific research also brings social and ethical challenges. Through a series of keynotes, panels, invited talks, paper presentations, and poster sessions, participants will have the opportunity to engage in cross-disciplinary discussions, share innovative ideas, and collaborate on solutions to current challenges.

Topics of Interest

Topics include, but are not limited to:

Autonomous AI Assistants for Scientific Research, including theoretical frameworks, system designs and case studies;
Datasets, Benchmarks and Evaluation Methods for AI-Assisted Scientific Research;
Human-AI Collaboration in Scientific Discovery, including design of novel user interfaces and workflows, and studies on how AI tools influence the cognitive processes of human researchers;
Social Impact and Ethical Considerations in AI-Assisted Scientific Research, including identification and mitigation of biases and hallucinations in AI assistants, ethical frameworks and safety guardrails, and perspectives and discussions in AI for scientific research.

Workshop Format

The workshop will be a one-day workshop, focusing on paper presentations, poster sessions, panels, and invited talks.

Attendance

All AAAI attendees who are interested in AI for scientific research are welcome.

Submission Guidelines

We welcome two types of papers:

Full papers: Full-length research papers from 4 to 7 pages (excluding references and appendices).
Short papers: research/position papers of up to 4 pages (excluding references and appendices).

All papers must be submitted in PDF format, using the AAAI-26 author kit. The review process will be single-blinded, and we welcome accepted and published papers. The contributions will be non-archival and only will be hosted on our workshop website. There will be one best paper award for accepted papers with outstanding quality.

The workshop uses Openreview for paper submission and review. The submission link is https://openreview.net/group?id=AAAI.org/2026/Workshop/AI4Research.

Important Dates

Submission Deadline: October 22, 2025
Notification of Acceptance: November 5, 2025

Organizing Committee

Hui Chen (contact person), National University of Singapore, hui.chen@nus.edu.sg
Yilun Zhao, Yale University, yilun.zhao@yale.edu
Manasi Patwardhan, TCS Research, manasi.patwardhan@tcs.com
Xiangliang Zhang, University of Notre Dame, xzhang33@nd.edu
See Kiong Ng, National University of Singapore, seekiong@nus.edu.sg
Arman Cohan, Yale University, arman.cohan@yale.edu
Bryan Hooi, National University of Singapore, bhooi@comp.nus.edu.sg
Tianyu Yang, University of Notre Dame, tyang4@nd.edu

Workshop Website:

https://ai4research-workshop.github.io

W11: Workshop on Multi-Agent Path Finding

Multi-Agent Path Finding (MAPF) involves computing collision-free paths for multiple agents from their starting locations to given destinations in a known environment. This problem finds diverse applications, from robot coordination to traffic management. Researchers in artificial intelligence, robotics, and theoretical computer science have been actively exploring various MAPF problem variants and solution approaches. This workshop aims to bring these researchers together to present their research, discuss future research directions, and cross-fertilise the different communities.

Topics of Interest

All topics related to multi-agent path finding or navigation are welcome, which include but are not limited to

Search- rule-, reduction-, reactive-, and learning-based planners
Planning and execution algorithms for dynamic or uncertain environments
Decentralised MAPF and mutli-agent reinforcement learning
Kinodynamically feasible multi-agent planning
Planning in continuous time and space
Customised MAPF planners for real robots
Cooperation in heterogeneous robot teams
Surveys of MAPF modeling, solvers, or benchmarks
Multi-robot or human-multi-robot collaboration
Multi-agent LLM systems with MAPF methods
Live demo of relevant software or hardware systems

Workshop Format

We plan to organise a one-day workshop that will include about two invited talks, a panel session, paper presentations, Q&A, a poster session, and a community discussion.

Attendance

The workshop has no maximum number of attendees; however, we are currently expecting to invite 50 participants, including program committees, accepted authors, invited speakers, and researchers who are active in the MAPF community. People who are not invited but are interested in MAPF are welcome to attend.

Submission Guidelines

Submission Requirements:

Submissions can contain relevant work in all possible stages, including work that was recently published, is under submission elsewhere, was only recently finished, or is still ongoing. Authors of papers published or under submission elsewhere are encouraged to submit the original papers or short versions (including abstracts) to educate other researchers about their work, as long as resubmissions are clearly labelled to avoid copyright violations. Position papers and surveys are also welcome. Submissions will go through a light review process to ensure a fit with the topic and acceptable quality. Non-archival workshop notes will be produced containing the material presented at the workshop.

Submission Format:

While there is no submission format or page limit requirement for the paper; however, the AAAI’26 format is strongly encouraged. Submission site: https://openreview.net/group?id=AAAI.org/2026/Workshop/WoMAPF#tab-your-consoles

Important Dates

Note: all deadlines are “anywhere on earth” (UTC-12)

Paper submission deadline: October 22, 2025
Paper notification: November 5, 2025
Final version: Dec 23, 2025
Workshop: Jan 26/27, 2026

Organizing Committee

Andy Li from Monash University, andy.li@monash.edu
Yorai Shaoul from Carnegie Mellon University, yshaoul@cs.cmu.edu
Chengyang He from the National University of Singapore, chengyanghe@u.nus.edu
Tanishq Duhan from the National University of Singapore, t.duhan@nus.edu.sg

Workshop Website:

https://mapf-workshop.github.io/aaai26

W12: Federated Learning for Critical Applications

This workshop focuses on the unique challenges of deploying FL systems in real-world, safety- or privacy-critical settings—environments where failure is not an option. Unlike existing FL workshops that emphasize general methods, FLCA emphasizes FL under realistic deployment constraints, including privacy guarantees, adversarial robustness, system-level failures, and regulatory compliance. Our emphasis is on practical FL systems that are scalable, robust, and aligned with real-world operational and infrastructural constraints.
This workshop benefits researchers from both industry and academia, exchanging theoretical knowledge and use-case scenarios that might help design federated learning systems for critical applications.
We encourage both applicative and theoretical contributions, including studies on specific settings and benchmarking tools.

Topics of Interest

Federated, distributed, and decentralized learning approaches for critical applications such as healthcare, advertising, blockchain, and social networks.
Infrastructure and system design for deploying real-world FL pipelines.
Techniques for FL across different learning paradigms, such as multi-task learning, meta-learning, semi-supervised learning, self-supervised learning, continual learning, …
Theoretical approaches with realistic assumptions for practical settings.
FL with heterogeneous and unbalanced (non-IID) data distributions.
Security and privacy in FL systems, including differential privacy, adversarial robustness, trustworthiness, and Machine Unlearning at scale.
Secure multi-party computation, trusted execution environments, and high-performance computing for federated computations.
Variants of FL and decentralized alternatives, including vertical FL, split learning, and gossip learning.
Other non-functional aspects in FL for critical use cases, such as fairness, explainability, and personalization.
Tools and resources (e.g., benchmark datasets, software libraries, …)

Workshop Format

This 1-day workshop will consist of keynote talks by prominent experts and technical presentations. The workshop will also include both oral and poster presentations of high-quality submissions not selected for oral sessions. The workshop will also include a panel session of industrial experts and academic researchers. If applicable, we issue a Best Paper Award that will get a special mention at the workshop’s conclusion.

Attendance

The workshop anticipates 50 participants, including academic researchers and industry professionals. There are no specific attendance criteria beyond the general AAAI registration requirements. The maximum number of attendees will be determined by the room capacity. Other AAAI attendees who are interested can also attend following AAAI’s related policy.

Submission Guidelines

We invite submissions of original research on the previously mentioned aspects of Federated Learning (see the complete list of topics). We accept both short papers (4 pages + references + optional appendix) and long papers (8 pages + references + optional appendix).
FLCA workshop does not have formal proceedings, i.e., it is non-archival. Accepted papers and their review comments will be posted on OpenReview in public (after the end of the review process), while rejected and withdrawn papers and their reviews will remain private.
We welcome submissions from novel research, ongoing (incomplete) projects, as well as recently published results.
Submission site information: OpenReview. The request for a submission site has already been made, but the link has not yet been provided by OpenReview. It will be provided as soon as possible.

Important Dates

Organizing Committee

-Linara Adilova, adylova.linara.r@gmail.com
-Bruno Casella, bruno.casella@unito.it
-Samuele Fonio, samuele.fonio@unito.it
-Michael Kamp, michael.kamp@tu-dortmund.de
-Mirko Polato, mirko.polato@unito.it

Workshop Website:

https://sites.google.com/view/flca

W13: AI in Drug Discovery: From Methods to Molecules

This workshop aims to connect algorithmic innovations in artificial intelligence (AI) with the practical challenges of discovering and developing new medicines. Emphasis is on methodologically rigorous strategies designed to resolve persistent challenges in drug development, specifically target identification, chemical space exploration, and the use of translational models to mitigate risk. By bringing together academic and industry experts from AI, computational life sciences, and pharmaceutical research, the workshop will foster cross-disciplinary exchange, showcase recent advances, and chart future directions at the interface of AI and drug discovery.

Topics of Interest

De-novo Drug Design: uses generative AI methods to construct new molecules that have the desired properties.
Active Learning: iteratively steers experiments to generate the most informative data that accelerate model improvements and enhance drug discovery efficiency.
Multi-Modal Learning: diverse data sources to build integrated AI models, enhancing drug discovery success rates.
Causal AI: moves beyond correlations to uncover causal relationships to derive actionable insights and enable the prediction of drug effects.

Workshop Format

The one-day workshop will feature four oral sessions, each focusing on a distinct research theme. Each session includes two invited presentations and two additional talks selected from submissions. The day will also include a poster session for submitted content and a final panel discussion with both senior and up-and-coming researchers, who will discuss future directions for the community.

Attendance

The sessions will be anchored by eight keynote talks from internationally recognized experts. To showcase emerging research, we will select an additional eight contributed oral presentations.

We invite everyone interested in advancing drug discovery via AI to attend, specifically:

AI researchers who wish to understand critical bottlenecks in drug discovery;
Drug discovery professionals who wish to be informed on the latest AI advances.

Due to venue constraints, priority will be given to contributors of the workshop.

Submission Guidelines

Submission Requirements

Full Paper, 6-8 pages + references: may be selected for oral presentation.

Short Paper, 2-4 pages + references: to be considered for poster presentation.

Submission Information

For submission information, please visit this link: https://submit.aidd.sg

Please direct all submission inquiries via email to: aaai26@aidd.sg

Organizing Committee

Workshop Chair

James Lu, A*STAR Bioinformatics Institute, james_lu@bii.a-star.edu.sg

Workshop Committee

James Lu, A*STAR Bioinformatics Institute, james_lu@bii.a-star.edu.sg

Roland Huber, A*STAR Bioinformatics Institute, rghuber@bii.a-star.edu.sg

Petra Schneider, ETH Zürich, petra.schneider@sl.ethz.ch

Workshop Website:

AI In Drug Discovery

W14: AI4EDU: AI for Education: On Opportunities and Challenges of Large Multimodal Models in Education

In the era of rapid advancements in artificial intelligence, multimodal AI systems — from large foundational models to efficient, task-specific agents – are reshaping how machines understand and generate human-like interactions across text, vision, audio, and beyond. While Large Language Model (LLM) and Large Multimodal Models (LMMs) offer unprecedented capabilities for personalization and automation in education, emerging paradigms such as Agentic AI and compact, efficient models are demonstrating remarkable efficacy in targeted educational tasks — often with greater deployability, interpretability, and alignment with real-world constraints. As these diverse AI approaches begin to permeate the educational landscape, they offer complementary pathways to enhance personalized learning, support diverse learner needs, and automate complex instructional tasks — while also raising critical questions about equity, transparency, and pedagogical alignment.

This workshop seeks to bring together leading researchers, practitioners, and policymakers to explore the multifaceted potentials and pressing challenges of applying multimodal and agentic AI systems — ranging from large foundational models to efficient, task-oriented agents — in education. By fostering dialogue across disciplines, the event aims to chart a roadmap for responsible innovation, ensuring that the deployment of these diverse AI approaches not only advances educational outcomes but also aligns with core human values in teaching and learning — including equity, accessibility, and sustainability.

Topics of Interest

We invite high-quality paper submissions of theoretical, empirical, or applied nature on topics at the intersection of Large Multimodal Models and Agentic AI with Education, including but not limited to:

Emerging technologies in education
Evaluation of education technologies
Immersive learning and multimedia applications
Self-adaptive learning
Individual and personalized education
Intelligent learning systems
Intelligent tutoring and monitoring systems
Automatic grading and assessment
Automated feedback and recommendations
Big data analytics for education
Analysis of communities of learning
Course development techniques
Data analytics & big data in education
Mining and web mining in education
Learning tools experiences and cases of study
Social media in education
Smart education
Digital libraries for learning
Knowledge management for learning
Learning technology for lifelong learning
Tracking learning activities
Wearable computing technology in e-learning
Smart classroom
Dropout prediction
Knowledge tracing
Multimodal Representation Learning for Education
Prompt Engineering and Instruction Tuning for Educational Tasks
Personalized and Adaptive Learning with Generative AI
Automated Curriculum, Lesson, and Assessment Design
Conversational and Interactive Educational Agents (e.g., AI Tutors, Virtual Classrooms)
Agentic Generative AI and Education
Vision-Language Applications in STEM, Humanities, and Language Education
Synthetic Data Generation and Simulation for Education Research
Cross-lingual, Cross-cultural, and Inclusive Educational Generation
Bias, Fairness, Robustness, and Equity in Generative Educational Systems
Explainability, Interpretability, and Trust in LMM Outputs for Educators and Learners
Evaluation Frameworks, Metrics, and Benchmarks for Educational LMMs
Human-AI Co-Creation and Collaboration in Teaching & Learning
Ethical, Legal, Societal, and Policy Implications of LMMs in Education
Teacher Augmentation, Professional Development, and Workflow Integration
Hallucination Mitigation and Pedagogical Alignment in AI-Generated Content

Workshop Format

We will engage the global AIED community through two primary channels:

Keynote Talks: We will invite established researchers to deliver visionary talks that (a) bridge communities across AI and education, (b) summarize mature research areas, or (c) propose bold new directions for LMMs in education.

Paper Submissions & Poster Sessions: We welcome submissions across a spectrum of formats — from early research ideas to deployed case studies — to be presented in spotlight talks or interactive poster sessions.

Attendance

We expect to attract around 100 attendees and 50 submissions. We are offering a small number of travel scholarships to promote attendance of unrepresented students/postdocs.

Submission Guidelines

The workshop solicits 5-7 pages double-blind paper submissions (with unlimited references) from participants. Submissions of the following flavors will be sought: (1) research ideas, (2) case studies (or deployed projects), (3) review papers, (4) best practice papers, and (5) lessons learned. The format is the standard double-column AAAI Proceedings Style. All submissions will be peer-reviewed.

Submission Site Information. The submission AUTHOR KIT can be found at https://aaai.org/authorkit25/. To ensure a fair review process, all submissions will be evaluated through a double-blind review.

Important Dates

(All deadlines are Anywhere on Earth, AOE)

September 10, 2025: OpenReview submission site opens for paper submissions

October 15, 2025: Abstracts due (11:59 PM AOE)

October 22, 2025: Full papers due (11:59 PM AOE)

November 13, 2025: Notification of final acceptance or rejection

December 13, 2025: Camera-ready files due

January 26–27, 2026: AI4EDU Workshop (Singapore EXPO)

Organizing Committee

Zitao Liu, Guangdong Institute of Smart Education, Jinan University

Christian M. Stracke, University of Bonn, Germany

Yu Lu, Beijing Normal University, China

Emmanuel G. Blanchard, University of Le Mans, France

Tianqiao Liu, TAL Education Group, China

Workshop Website:

https://ai4ed.cc/workshops/aaai2026; For any questions email Tianqiao Liu at liutianqiao1@tal.com.

W15: AIR-FM: Assessing and Improving Reliability of Foundation Models in the Real World

Despite remarkable advances in capability, foundation models such as LLMs and VLMs face fundamental challenges in maintaining reliability under real-world conditions. Their stochastic nature and sensitivity to context make them vulnerable to distribution shifts, sensor noise, hallucinations, overconfidence, and prompt variability. These issues limit safe deployment in critical domains like healthcare, law, robotics, and autonomous driving.

This workshop will serve as a forum for researchers and practitioners to discuss definitions, metrics, and methods for reliability quantification, explore principled evaluation frameworks, and propose strategies to enhance robustness and trustworthiness across language and vision tasks. By bridging the LLM and VLM communities, we aim to foster cross-domain insights, stimulate the creation of realistic stress-test datasets, and encourage approaches that ensure dependable performance in operational settings.

Topics of Interest

We invite contributions, including, but not limited to:

Failure Mode Analysis: Characterising unreliability in LLMs and VLMs under real-world conditions, including domain shifts, adversarial inputs, and sensor degradations.
Reliability-Centered Datasets: Designing datasets to expose vulnerabilities, long-tail phenomena, or multi-modal inconsistencies.
Metrics and Evaluation Frameworks: Developing measures that capture robustness, calibration, and generalization beyond accuracy or average precision.
Reliability-Aware Architectures and Training: Model designs and learning paradigms that explicitly target dependable performance in realistic scenarios.
Uncertainty Estimation and Detection: Predicting, detecting, and mitigating unreliable outputs before deployment.
Security, Hallucination, and Prompt Sensitivity: Red-teaming, jailbreak detection, and methods to reduce context-driven unreliability.
Cross-Domain Reliability Insights: Lessons and techniques transferable between language-only, vision-only, and multi-modal systems.

Workshop Format

This is a one-day workshop.

We plan the following:

Keynote talks with discussion time.
Panel Discussion with discussion time.
Spotlight presentations for selected submissions. Scheduled right before their respective poster sessions, allowing for discussion at the posters.
Poster presentations for all accepted submissions.

Attendance

Given that the topic might be highly engaging for researchers from numerous communities, we expect to attract around 250 attendees and 50 submissions.

Submission Guidelines

All submissions are to be made in the AAAI 2026 Format.

Please follow the author guidelines set by AAAI 2026.

We are accepting submissions in the following three tracks:

Archival Track: 8 pages, accepted papers hosted on OpenReview

Non-archival Track: 2-8 pages (usually 4 pages), this is an extended abstract.

Tutorials Track: a 4-page description of the intended tutorial, with a good motivation, plan of action, and tutorial content.

The archival and non-archival track submissions are double-blind.

The tutorial submissions are single-blind.

Submission Link: https://openreview.net/group?id=AAAI.org/2026/Workshop/AIR-FM

Important Dates

All deadlines are 11:59 PM AoE.

Submission Deadline: October 15, 2025

Notification of Acceptance: November 5, 2025

Camera-Ready Deadline: December 10, 2025

Workshop Date: January 26, 2026.

Organizing Committee

Shashank Agnihotri – PhD Candidate, University of Mannheim (Main contact) shashank.agnihotri@uni-mannheim.de
Desi R. Ivanova – Research Fellow, Department of Statistics, University of Oxford (Main contact) desi.r.ivanova@gmail.com
Dr. Julia Grabinski – Postdoctoral Researcher, University of Darmstadt julia.grabinski@visinf.tu-darmstadt.de
Wei Liu – Research Fellow, National University of Singapore weiliumg@gmail.com
Harit Vishwakarma – Incoming Postdoctoral Research Fellow, Department of Statistics, University of Oxford harit.vishwakarma@gmail.com
Dr. Jindong Gu – Senior Researcher, Google Research & DeepMind jindong.gu@outlook.com
Dr. Vikash Sehwag – Research Scientist, Google DeepMind sehwag.vikash@gmail.com
Abhipsa Basu – PhD Student, Indian Institute of Science abhipsabasu@iisc.ac.in
Jenny Schmalfuss – Doctoral Researcher, University of Stuttgart jenny.schmalfuss@vis.uni-stuttgart.de
Dr. Francesco Croce – Postdoctoral Researcher, École Polytechnique Fédérale de Lausanne (EPFL) francesco91.croce@gmail.com
Dr. Sravanti Addepalli – Research Scientist, Google DeepMind sravantia@google.com
Prof. Wee Sun Lee – Professor, National University of Singapore dcsleews@nus.edu.sg
Prof. Luke Ong – Professor, Nanyang Technological University luke.ong@ntu.edu.sg
Prof. Dr. R. Venkatesh Babu – Professor, Indian Institute of Science venky@iisc.ac.in
Prof. Yee Whye Teh – Professor, University of Oxford yeewhye.ox@gmail.com
Prof. Dr.-Ing Margret Keuper – Professor, University of Mannheim & Max-Planck-Institut für Informatik keuper@uni-mannheim.de

Workshop Website:

https://llmrel.github.io

W16: Artificial Intelligence for Air Transportation (AI4AT)

Academic, industrial, and governmental interest in applying artificial intelligence (AI) to air transportation has accelerated markedly in recent years, fueled by the urgent need to make aviation systems more efficient, resilient, and environmentally sustainable. The inaugural Workshop on Artificial Intelligence for Air Transportation (AI4AT) will bring together researchers and practitioners to explore how AI can address pressing challenges across air traffic management, airport operations, advanced air mobility, and aviation safety. Held in conjunction with AAAI-26 in Singapore — a global hub for aviation innovation and regulatory leadership — AI4AT will provide a dynamic forum for interdisciplinary exchange and collaboration.

Topics of Interest

The workshop will focus on a broad range of issues, including but not limited to:

Air Traffic Management (ATM): AI-enabled solutions for improving air traffic control and optimizing airspace usage, crucial for handling growing flight volumes and increasingly complex weather patterns caused by climate change.
Airport Operations: AI-enabled solutions for more efficient and smoother ground operations, to handle growing passenger numbers.
Advanced Air Mobility (AAM): The use of AI for both vehicular autonomy and traffic management in the urban landscape.
Aviation Safety: The use of AI for enhancing aviation safety, but also the role of trustworthy AI in a high-stakes application domain.

Workshop Format

This is a 1-day non-archival workshop featuring invited keynotes, spotlight talks, and a poster & networking session.

Attendance

All AAAI-26 attendees are welcome to participate. A total of S$600 in prizes will be awarded to the best spotlight and poster presentations.

Submission Guidelines

Extended abstracts of up to 2 pages (excluding references), using the AAAI-26 Author Kit. Submissions may be unpublished, in-progress, concurrently submitted, or recently published works.

Submissions will be managed on the OpenReview platform. Each submission will undergo a peer review process. Review criteria will include relevance to the workshop theme, technical soundness, originality, and potential to spark meaningful discussion during the workshop. Submissions and reviews will not be made public.

Important Dates

The submission deadline is October 22nd, 2025. A submission link will be made available on the workshop website closer to the deadline.

Organizing Committee

Joewie J. Koh (The MITRE Corporation), jkoh@mitre-ap.sg
Yicheng Zhang (Institute for Infocomm Research, A*STAR), zhang_yicheng@i2r.a-star.edu.sg
Yifang Yin (Institute for Infocomm Research, A*STAR), yin_yifang@i2r.a-star.edu.sg
Nuno Antunes Ribeiro (Singapore University of Technology and Design), nuno_ribeiro@sutd.edu.sg
Hasnain Ali (Nanyang Technological University), hasnain.ali@ntu.edu.sg

Workshop Website:

https://ai4at.github.io

W17: Artificial Intelligence for Time Series Analysis (AI4TS): Theory, Algorithms, and Applications

Time series data are becoming ubiquitous in numerous real-world applications, e.g., IoT devices, healthcare, wearable devices, smart vehicles, financial markets, biological sciences, environmental sciences, etc. Given the availability of massive amounts of data, their complex underlying structures/distributions, together with the high-performance computing platforms, there is a great demand for developing new theories and algorithms to tackle fundamental challenges (e.g., representation, classification, prediction, causal analysis, etc.) in various types of applications.

The goal of this workshop is to provide a platform for researchers and AI practitioners from both academia and industry to discuss potential research directions, key technical issues, and present solutions to tackle related challenges in practical applications. The workshop will focus on both the theoretical and practical aspects of time series data analysis and aims to trigger research innovations in theories, algorithms, and applications. We will invite researchers, AI practitioners, and policymakers from the related areas of machine learning, data science, statistics, econometrics, and many others to contribute to this workshop.

Topics:

This workshop encourages submissions of innovative solutions for a broad range of time series analysis problems. Topics of interest include but are not limited to the following:

Time series forecasting and prediction
Spatio-temporal forecasting and prediction
Time series anomaly detection and diagnosis
Time series change point detection
Time series classification and clustering
Time series similarity search
Time series similarity search
Time series indexing
Time series compression
Time series pattern discovery
Time series pattern discovery
Interpretation and explanation in time series
Causal discovery and inference in time series
Bias and fairness in time series
Benchmarks, experimental evaluation, and comparison for time series analysis tasks
Foundation models and LLMs for time series analysis
Multimodal time series analysis
Agentic AI for time series analysis
Time series applications in various areas: E-commerce, Cloud computing, Transportation, Fintech, Healthcare, Internet of things, Wireless networks, Predictive maintenance, Education, Energy, Climate, etc.

Important Dates:

October 22, 2025 – Submission Deadline
November 5, 2025 – Acceptance Notification
January 26-27, 2025 – Workshop Date

Attendance:

Researchers, students, and practitioners in AI, machine learning, data mining, and time series analysis.

Submission Requirements:

Submissions should be 4-7 pages long, excluding references, and follow AAAI 2026 template. Submissions are double-blind and author identity should not be revealed to the reviewers. An optional appendix of arbitrary length is allowed and should be put at the end of the paper (after references).

Workshop Organizers:

Min Wu, Institute for Infocomm Research, A*STAR
Dongjin Song, University of Connecticut  
Qingsong Wen, Squirrel Ai Learning 
Cong Shen, University of Virginia  
Sanjay Purushotham, University of Maryland Baltimore County  
Shirui Pan, Griffith University
Stefan Zohren, University of Oxford
Niloy Biswas, Meta 
Haifeng Chen, NEC Labs America, Princeton  
Yuriy Nevmyvaka, Morgan Stanley
Xiaoli Li, Institute for Infocomm Research, A*STAR

Workshop Website:

https://ai4ts.github.io/aaai2026

W18: Audio-Centric AI: Towards Real-World Multimodal Reasoning and Application Use Cases

This Audio-aaai workshop focuses on recent and emerging directions in the field of audio understanding, particularly beyond conventional tasks such as sound event detection (SED) and acoustic scene classification (ASC).

Topics of Interest

The topics of interest include, but are not limited to:

Robust audio and multimodal scene analysis under real-world constraints (e.g., asynchronous microphones, reverberant environments, video-audio desynchronization)
Data augmentation and synthetic generation for spatial audio and video-audio learning
Multimodal large-scale models (LLMs, VLMs) for audio-language(-video) retrieval and understanding
Perceptual representation learning for 3D sound event localization, detection, and audio-visual grounding
Foundation models and adaptation for audio, speech, and video-audio tasks
Speech, audio, and video-conditioned generation (avatars, dubbing, cross-modal synthesis)
Audio-Visual scene understanding and reasoning
Audio and speech quality assessment: evaluation, metrics, and perception-driven benchmarks
Multimodal safeguard and robustness in audio-video-language modeling
Efficient evaluation frameworks for reasoning, generation, and multimodal integration (LLM + audio + video)
Benchmarking dataset creation and sharing across audio, speech, and video modalities
Applications of microphone array processing and virtual microphone techniques in multimodal systems
Real World Applications: entertainment & media (karaoke, video avatars), manufacturing (process monitoring), sustainability (forest restoration, biodiversity monitoring), education (classroom video-audio analysis), healthcare (elderly care monitoring), security (robotic patrolling, surveillance with video-audio fusion)

Workshop Format

The workshop is a one-day event for balancing depth and participant engagement, including invited talks, paper presentations and discussions.

Attendance

We expect to attract around 50 attendees and 20 submissions.

Submission Guidelines

Please follow the template required by AAAI at https://aaai.org/conference/aaai/aaai-26/main-technical-track-call/ for full paper (8 pages except references) and short paper (4 pages except references). The review process will be double-blind. The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.

Submission Site: http://cmt3.research.microsoft.com/AudioAAAI2026/

Important Dates

24 October 2025: Workshop Paper Submission Due

 5 November 2025: Notification Sent to Authors

16 November 2025: Early Bird Registration

26 January 2026: Workshop Program

Organizing Committee

Nancy F. Chen, A*STAR Institute for Infocomm Research (A*STAR I²R), Singapore, nfychen@i2r.a-star.edu.sg
Nobutaka Ono, Tokyo Metropolitan University, Japan, onono@tmu.ac.jp
Xiaoxue Gao, A*STAR Institute for Infocomm Research (A*STAR I²R), Singapore, Gao_Xiaoxue@i2r.a-star.edu.sg

Keisuke Imoto, Kyoto University, Japan, keisuke.imoto@ieee.org
Tatsuya Komatsu, LY Corporation, Japan, komatsu.tatsuya@lycorp.co.jp

Workshop Website:

https://sites.google.com/view/audio-aaai/home

W19: Automated Spatial and Temporal Anomaly Detection (ASTAD)

The 3rd Workshop on Automated Spatial and Temporal Anomaly Detection (ASTAD) at AAAI-26 is a premier platform for researchers and practitioners at the forefront of AI-driven anomaly detection. With the proliferation of spatiotemporal data from sources like surveillance cameras, IoT sensors, and satellite imagery, the need for robust, automated systems to identify novel and unusual patterns has never been more critical. This workshop will delve into cutting-edge AI techniques that move beyond traditional rule-based methods to uncover hidden anomalies, fostering a new generation of intelligent monitoring and discovery. Join us to discuss how we can build more resilient, autonomous, and insightful systems for a safer and more efficient world.

Topics of Interest

We invite submissions on a wide range of topics, including but not limited to:

Novel deep learning and generative models for spatiotemporal anomaly detection.
Self-supervised, unsupervised, and few-shot learning for anomaly detection with limited data.
Continual and lifelong learning approaches for evolving anomalous patterns.
Explainable AI (XAI) for interpreting and justifying anomaly detection decisions.
Foundation models for anomaly detection, including large language models (LLMs) and vision–language models (VLMs).
On-edge anomaly detection in real-time and resource-constrained settings.
Novel datasets, benchmarks, and evaluation metrics.
Applications in computer vision, robotics, autonomous driving, predictive maintenance, healthcare, finance, and beyond.

Workshop Format

ASTAD will be a dynamic one-day event designed for maximum engagement and knowledge exchange. The format will include a mix of paper presentations, invited talks from leading researchers, and interactive poster sessions. We aim to spark lively discussion and debate by dedicating ample time to Q&A and a concluding panel discussion with organizers and invited speakers. This structure is designed to not only showcase new research but also to build a vibrant community around this critical topic.

Attendance

Attendance is open to all registered participants of the AAAI-26 conference. We encourage anyone with an interest in the topic to attend, whether you are a researcher, a student, or an industry professional.

Submission Guidelines

We welcome original research contributions as full papers, in line with the standards of AAAI main track format. Submissions may consist of up to 7 pages of technical content plus additional pages solely for references. All submissions must be formatted using the official AAAI-26 author kit and will be subject to a double-blind peer-review process. Accepted papers will be included in the non-archival workshop proceedings, allowing authors to pursue future publications.

Important Dates

Paper Submission Due: October 22, 2025
Notifications Sent to Authors: November 5, 2025
Workshop to be held on January 26th, 2026

Organizing Committee

Workshop Chair: Narges Armanfard – narges.armanfard@mcgill.ca

Workshop Committee:

Narges Armanfard – narges.armanfard@mcgill.ca
Thi Kieu Khanh Ho – thi.k.ho@mcgill.ca
Hadi Hojjati – hadi.hojjati@mcgill.ca

Workshop Website:

https://ismart.ece.mcgill.ca/astad3

W20: Bodily Expressed Emotion Understanding (BEEU) 2026

Humans inherently communicate through body movement. Bodily expressions are a fundamental, often subconscious, way of responding to social situations and expressing emotions. Despite extensive studies in psychology, human behavior, dance, and theater, computational interpretation of body language, to predict internal states such as emotions or intention, remains challenging and underexplored. Addressing this gap holds significant potential across various fields: from socially adept robots and intuitive AI interfaces to holistic AI systems approaching human-level physical intelligence. The 2nd International Workshop on Bodily Expressed Emotion Understanding welcomes discussions on approaches and latest results on modeling bodily expression automatically. A primary focus will be on predicting emotions and emotion-related behavior, while research on related tasks and applications (e.g., activity recognition, generation of socially appropriate behavior, embodied intelligence, etc.) will also be strongly encouraged to be shared.

Topics of Interest

We encourage research and discussions on any of the following topics:

Automatic Modeling of Body Language
Emotion Recognition using Body Language
Activity Recognition using Body Language
Emotion Understanding Beyond Facial Expression
Emotional Gesture Generation
Applications in Robotics, HRI, or related fields
Use of Large Foundation Models for Emotion Recognition, Expression, Generation
Data sharing and open-science with human subjects data
Expressive human pose representation
Human movements coding system
Algorithmic fairness and data ethics related to emotion modeling

Workshop Format

The workshop will include the following components: oral paper presentations, lightning talk sessions, keynote talks by invited speakers, and a panel discussion. The workshop will also have a challenge component, and the presentations can include top submissions made to our challenge track. It will be a 1-day event.

Submission Guidelines

We invite submissions on any of the topics of interest (or adjacent topics) in two different formats:

2-page Extended Abstracts: these submissions will be selected to provide a ~5-minute lightning talk
6-8 pages full papers: these submissions will be selected for oral presentations of about 15 minutes.

We welcome the submission of in-progress, unpublished, or already published work (subject to the policies of the original publishing venue). The papers submitted can be independently of any of the topics mentioned previously, or describe the methods used to solve our emotion understanding challenge. More information about the challenge will be made available on our website.

Submission Site Information:

Submissions will be through OpenReview. Please submit papers here: https://openreview.net/group?id=AAAI.org/2026/Workshop/BEEU

Organizing Committee

Workshop Chair: James Z. Wang, Penn State
Workshop Committee:

Sree Bhattacharyya, PhD student, Penn State (primary contact): sfb6038@psu.edu
James Z. Wang, Distinguished Professor, Penn State: jzw11@psu.edu
Amy LaViers, Director, The RAD Lab: amy@the-rad-lab.org
Reg Adams, Professor, Penn State: rba10@psu.edu
Rachelle Tsachor, Associate Professor, University of Illinois, Chicago: rtsachor@uic.edu

Other organizers and contributors include: Sitao Zhang, PhD student, Penn State (sitao.zhang@psu.edu), Jonathan Chen, undergrad student, Penn State (jqc6822@psu.edu), Justin Lokos, independent contributor (jtl5503@psu.edu).

Workshop Website:

W21: Bridging Neurons and Symbols for NLP and Knowledge Graph Reasoning

This Third workshop of NeusymBridge @AAAI 2026 (Bridging Neurons and Symbols for NLP and Knowledge Graph Reasoning, abbreviated as NeusymBridge below) focuses on the integration of symbolic knowledge and neural models to address challenges in natural language processing (NLP) and knowledge reasoning. The workshop brings together researchers from both academia and industry to explore cutting-edge approaches in neurosymbolic systems, aiming to enhance large language models (LLMs) with more robust reasoning and knowledge representation capabilities. We invites the submission of long and short papers featuring substantial, original, and unpublished research on Natural Language Processing and Knowledge Graph reasoning

Topics of Interest

The workshop invites submissions on a wide range of topics, including but not limited to:

Proposing novel knowledge representations that are derived from transdisciplinary research
Using knowledge graphs or other types of symbolic Knowledge to improve the quality of LLMs
Neuro-symbolic parsing methods for NLP
Using symbolic structures, or natural languages, to interpret the reasoning mechanism of pre-trained language models
Exploring the reasoning mechanism of LLMs
Distilling symbolic knowledge from LLMs
Proposing benchmark datasets and evaluation matrices for neuro-symbolic approaches to NLP tasks
Proposing novel NLP tasks for neuro-symbolic approaches
NLP applications in classification, sense-disambiguation, sentiment analysis, question-answering, knowledge graph reasoning
Critical analysis of traditional deep learning or LLMs
Analysing spatial reasoning of LLMs
Proposing novel neural computing that may reach symbolic-level reasoning
Proposing benchmark datasets and matrices to evaluate the gap between neural reasoning and symbolic reasoning
Addressing efficiency issues in neuro-symbolic systems
Identifying challenges and opportunities of neuro-symbolic systems
Developing retrieval augmented models for combining KG and LLMs
Applying neuro-symbolic approaches to humor generation and other real-life applications
Datasets and evaluation matrices supporting both neural and symbolic approaches to NLP tasks
New meaning representation for with both symbolic and neural meaning representation
New neural architecture for both heuristic reasoning with data and rational reasoning with symbolic rules
Innovative applications of hybrid models combining symbolic and neural architectures

We will select 2-3 outstanding papers based on reviewer feedback.

Workshop Format

Please use the template provided by AAAI 2026 using the following link: AAAI 2026 PAPER TYPES AND FORMATS.

Submission Guidelines

NeusymBridge Workshop @ AAAI 2026 (abbreviated as NeusymBridge below) invites the submission of long papers of up to eight pages and short papers of up to four pages. These page limits only apply to the main body of the paper. At the end of the paper (after the conclusions but before the references) papers need to include a mandatory section discussing the limitations of the work and, optionally, a section discussing ethical considerations. Papers can include unlimited pages of references and an unlimited appendix. Authors should follow the general instructions for AAAI 2026 proceedings, which are an adaptation of the general instructions for \*AAAI proceedings.

Submission Link: https://openreview.net/group?id=AAAI.org/2026/Workshop/NeusymBridge#tab-your-consoles

Important Dates

All deadlines are 11:59PM UTC-12:00 (“anywhere on Earth”)
Submission Deadline: November 15, 2025
Notification of Acceptance: December 1, 2025
Camera-Ready papers due: December 13, 2025
Workshop Day: January 26, 2026

Organizing Committee

Yunfei Long (Chair):Queen Mary University of London (qp241311@qmul.ac.uk);
Yansong Feng (Publishing chair) : Peking University (fengyansong@pku.edu.cn);
Xianpei Han (Publicity chair) : Chinese Academy of Sciences (xianpei@iscas.ac.cn);
Shizhu He (Publicity chair) : Chinese Academy of Sciences (shizhu.he@ia.ac.cn);
Tiansi Dong (Contact chair) University of Cambridge (td540@cam.ac.uk);

W22: Consistency in Video Generative Models: from Clip to Wild (CVM @ AAAI ’26)

Video generative models have revolutionized creative industries, yet a pivotal challenge remains: ensuring consistency. Inconsistencies in generated content, from violations of physical laws within a single clip to shifting character appearances across shots, disrupt realism and undermine narrative coherence.

This workshop will address the critical issue of consistency in video generation through four key dimensions: (1) Intra-clip world knowledge consistency, ensuring logical coherence within clips; (2) Inter-clip camera consistency, maintaining seamless transitions across camera movements; (3) Inter-shot element consistency, preserving the identity of characters and objects; and (4) Human-in-the-loop preference consistency, aligning generated content with user expectations. Our main objective is to bring together researchers and practitioners to foster collaboration, establish benchmarks, and develop robust methodologies for creating trustworthy and credible video generative systems.

Topics of Interest

We encourage contributions related to, but not limited to: Datasets and Benchmarks for Video Generation; Consistency in Video Generation Models (including intra-clip knowledge, inter-clip camera, inter-shot element, and human-in-the-loop preference consistency); Advanced video generation model architectures; Knowledge-driven consistency modeling; and Human preference alignment using methods like RLHF.

Workshop Format

The CVM workshop will be a full-day hybrid event to ensure broad participation. The program will be highly interactive, featuring keynote speeches, invited talks, paper presentations, and panel discussions. A key highlight will be a “Consistency Video Generation Competition” designed to advance and standardize the evaluation of consistency in generative video models.

Attendance

We invite all researchers and practitioners from academia and industry working in generative AI, computer vision, and multimodal learning to attend. The workshop aims to attract a diverse audience to tackle one of the most pressing challenges in the field.

Submission Guidelines

Submissions of technical papers can be up to 7 pages excluding references and appendices. Short or position papers of up to 4 pages are also welcome. All papers must be submitted in PDF format, following the AAAI 2025 format. Papers will be peer-reviewed (single-blind). The submission link URL will be announced in our workshop website (https://sites.google.com/view/aaai26-cvm).

Organizing Committee

Workshop Chair

Li Yuan, Peking University, yuanli-ece@pku.edu.cn
Qibin Hou, Nankai University, houqb@nankai.edu.cn
Long Chen, HKUST, longchen@ust.hk
Wenhan Luo, HKUST, whluo@ust.hk
Ming-Ming Cheng, Nankai University, cmm@nankai.edu.cn
Shuicheng Yan, National University of Singapore, yansc@nus.edu.sg
Philip Torr, University of Oxford, philip.torr@eng.ox.ac.uk

Working Committee

Yupeng Zhou, Nankai University, ypzhou@mail.nankai.edu.cn
Zixuan Ye, HKUST, zixuan.ye@connect.ust.hk
Hongxiang Li, HKUST, hlihg@connect.ust.hk
Yuwei Niu, Peking University, hlihg@connect.ust.hk
Yuan Wang, Nankai University, hangfrieddays@gmail.com

Workshop Website:

https://sites.google.com/view/aaai26-cvm

W23: Creative AI for Live Interactive Performances

This workshop brings together researchers exploring interactive creative AI systems that collaborate with humans across performing arts including music, visual arts, dance, and drama. Our central focus is designing AI systems that meaningfully participate in real-time creative workflows to enhance human performance. Unlike traditional generative AI producing finished outputs, interactive AI systems must respond dynamically to human input, adapt to context, and facilitate ongoing human-AI collaboration.

The workshop addresses unique technical and methodological challenges arising when AI systems interact with humans during creative processes. Key questions include:

How can AI respond to improvised and subjective performance in real-time?
What interaction paradigms best support human-AI collaborative creativity?
How do we evaluate systems where novelty and improvisation may be valued above correctness?

Topics of Interest

Specific areas of interest include but are not limited to:

AI for Interactive Performance (HCI+AI)
Human-robot creative interaction (HRI)
Creative interactive applications for live performances
Interactive art through Brain-computer interfaces (BCI+generative art)
Real-world applications/pervasive computing for creative AI
Personalised generation and tuning of music
Evaluation of creative AI
Social, cultural, legal, and ethical considerations including trustworthiness, copyright, and authorship

Workshop Format

This 1-day workshop will open with a keynote on AI for live performances, followed by paper presentations (10 minutes + 5 minutes questions) grouped by interaction modalities and a second keynote from a performing artist will provide real-world perspective. The afternoon features a poster session for the remaining papers, structured group discussion on future research directions and ethics, concluding with interactive demonstrations. Participants are invited to demonstrate their work physically or through a video show-and-tell presentation.

Attendance

Open to all attendees based on invitation. All presentations must be in person, virtual Presentations are not permitted.   Expected attendance is up to 30 participants.

Submission Guidelines

We invite original research papers, position papers, and case studies (up to 6 pages excluding references) following AAAI 2026 format. Work-in-progress welcome. Authors encouraged to include prototype implementation descriptions as well as bring along either a demo or video demo of the work for the show-and-tell session.

Important Dates

Deadline: 22nd October 2025 AoE

Notifications Sent to Authors: 5th November 2025

Organizing Committee

Workshop Chair: Dr. Kieran Woodward, University of Nottingham, kieran.woodward1@nottingham.ac.uk

Prof. Steve Benford, University of Nottingham, steve.benford@nottingham.ac.uk

Dr. Alicia Falcon-Caro, University of Nottingham / Nottingham Trent University, alicia.falconcaro@nottingham.ac.uk

Dr. Richard Ramchurn, University of Nottingham, richard.ramchurn@nottingham.ac.uk

Workshop Website:

https://sites.google.com/view/aaai26clip

W24: Deployable AI Workshop

Artificial Intelligence (AI) has rapidly evolved into a multifaceted research domain, with recent generative models like Gemini, GPT-5, Claude, Llama, DeepSeek, Qwen, and others demonstrating remarkable capabilities across diverse tasks. While their potential is immense, real-world deployment requires addressing not only important technical challenges but also ethical and societal ones. It also requires several fundamental research questions and issues, involving algorithmic, systemic, and societal aspects, to be addressed.

This workshop will address these critical research questions for responsible AI deployment. Specifically, the workshop will focus on algorithmic, systemic, and societal considerations to ensure that our AI models adhere to rigorous standards for fairness, ethics, explainability, privacy, and security.

Topics of Interest

We invite submissions on the theoretical foundations, algorithmic techniques, and practical strategies to ensure the responsible deployment of AI models. The topics of interest include, but are not limited to, the following:

Deployable AI: Concepts and Models
Privacy-Preserving AI in the era of GenAI
Language Models & Deployability
Explainable and Interpretable AI
Fairness and Ethics in AI
AI models and social impact
Trustworthy AI models

Workshop Format

Invited Talks: Talks by eminent researchers in the field.

Contributed Talks: Short talks based on the accepted papers.

Poster session: Poster presentation of all accepted papers

Submission Instructions:

You are invited to submit:

– Poster/short/position papers (up to 4 pages)

– Full papers (up to 7 pages)

-The submissions should adhere to the AAAI paper guidelines available at

https://aaai.org/conference/aaai/aaai-26/submission-instructions/

Attendance

Submission Guidelines

The submissions should adhere to the AAAI paper guidelines available at https://aaai.org/conference/aaai/aaai-26/submission-instructions/

Important Dates

Workshop Submissions Due to Organizers: October 22, 2025

Notifications Sent to Authors: November 5, 2025

Workshop to be held on January 26th/27th, 2026

Organizing Committee

1. Balaraman Ravindran(Senior member AAAI)

Chair and main contact, ravi@dsai.iitm.ac.in, Wadhwani School of Data Science & AI, IIT Madras

2. Krishna Pillutla, krishnap@dsai.iitm.ac.in, Wadhwani School of Data Science & AI, IIT Madras

3. Arpita Biswas arpita.biswas@live.in, Department of Computer Science, Rutgers University

4. Aravindan Raghuveer araghuveer@google.com, Google DeepMind India.

Working Committee:

1. Arun Rajkumar, arunr@dsai.iitm.ac.in, Wadhwani School of DSAI, IIT Madras.

2. Harish Guruprasad, hariguru@dsai.iitm.ac.in, Wadhwani School of DSAI, IIT Madras.

3. Chandrashekar Lakshminarayanan, chandrashekar@dsai.iitm.ac.in, Wadhwani School of DSAI, IIT Madras

4. Gokul S Krishnan, gokul@cerai.in, CeRAI, IIT Madras.

5. Devika Jay, devikajay@gmail.com, CeRAI, IIT Madras.

6. Rahul Vashisht, rahul@cse.iitm.ac.in, PhD scholar, IIT Madras

Workshop Website:

The 4th Deployable AI Workshop

W25: Emerging AI Technologies for Music

This full day workshop, part of a new annual series on the latest developments in AI for music, explores how AI can become a true creative collaborator rather than just another automation tool. As AI transforms music composition, performance, and production, we’re seeing an exciting shift from fully autonomous systems toward human-centered approaches. We aim to explore the latest research in human-centric AI for music and discuss how systems can empower humans to shape AI outputs through intuitive interfaces, interactive workflows, interpretable systems and meaningful user-controls. As the field is increasingly shifting toward co-creative and controllable systems, we’ll dive into cutting-edge research and discuss how meaningful musical outcomes can arise through human-machine collaboration. Join us for this inaugural workshop as we explore how to design AI that enhances rather than overshadows human musical creativity.

Topics of Interest

Topics of Interest include, but are not limited to:

Music composition and generation
Foundational music models
Music production workflows
Music education and pedagogy
Music performance systems
Music information retrieval
Sound design and audio production
Music therapy applications
Music transcription and analysis
Singing voice synthesis
Evaluation of music AI systems
Music recommender systems
Musical instrument design
Robotic musicianship
Optical music recognition
Music theory and musicology
Ethical, cultural and societal implications
Accessibility and inclusion in music AI

Workshop Format

This is a full-day workshop from 9AM to 5PM. The workshop will include invited speaker talks, paper presentations, a poster session, and a panel discussion.

Attendance

What are the criteria to be invited: Keynote speakers include experts in the topic. To give a talk, we invite you to submit a paper. The workshop is open to any attendee who has registered at AAAI-26.
At this moment, there is no limit on the maximum number of attendees. The workshop organizers will communicate with the conference organizers if the number of attendees exceeds the expectation.

Submission Guidelines

We welcome submissions of original research, creative systems, and critical perspectives on emerging AI technologies for music. Contributions may include theoretical work, empirical studies, system designs, evaluations, or artistic explorations. While implemented systems and demonstrations are encouraged, strong conceptual and analytical contributions without a prototype are also welcome. We particularly value submissions that emphasize controllability, interpretability, explainability, personalization, human-AI interaction, and collaboration in music systems.

Paper Format: Maximum of 8 pages (excluding references). Please follow the PMLR format with the template on our website: https://amaai-lab.github.io/EAIM2026/

Submission Site Information: https://openreview.net/group?id=AAAI.org/2026/Workshop/EAIM

Accepted Papers will be posted at the workshop website and published as a volume in Proceedings of Machine Learning Research (PMLR).

Important Dates

Submission: October 24, 2025
Notification of Acceptance: November 14, 2025
Final Version: December 14, 2025

Workshop Chair

Keshav Bhandari, k.bhandari@qmul.ac.uk

Workshop Committee

Keshav Bhandari, Queen Mary University of London, k.bhandari@qmul.ac.uk
Abhinaba Roy, Singapore University of Technology and Design, abhinaba_roy@sutd.edu.sg
Dorien Herremans, Singapore University of Technology and Design, dorien_herremans@sutd.edu.sg
Simon Colton, Queen Mary University of London, s.colton@qmul.ac.uk

Workshop Website:

https://amaai-lab.github.io/EAIM2026

W26: AI Governance Workshop: Alignment, Morality, Law, and Design

The AI Governance Workshop addresses the critical intersection of artificial intelligence (AI) governance, ethical alignment, and responsible deployment across autonomous systems. As AI technologies rapidly advance from generative models to sophisticated agentic systems capable of autonomous decision-making, new governance challenges emerge that existing frameworks cannot adequately address. This workshop brings together researchers, industry practitioners, and policy experts to develop actionable frameworks for safe, aligned, and accountable AI systems. Our unique focus combines technical governance mechanisms with policy integration, emphasizing “Governance by Design” approaches that embed responsibility directly into AI architectures rather than applying governance as external oversight.

Topics of Interest

We invite submissions addressing AI governance challenges, including:

Technical AI Governance: Alignment methods, safety mechanisms, verification frameworks for autonomous systems.
Policy and Regulatory Frameworks: Legal compliance, regulatory approaches for AI deployment, and international governance standards.
Ethical AI Implementation: Bias mitigation, fairness, transparency, and accountability in AI decision-making.
Agentic AI Governance: Governance frameworks specifically for autonomous decision-making systems.
Human-AI Collaboration: Oversight models, collaborative approaches between humans and AI systems.
Industry Applications: Case studies, deployment experiences, practical governance implementation.
Explainable AI: Decision transparency, interpretability methods for governance compliance.
Multi-agent Coordination: Governance mechanisms for multi-agent systems and distributed AI architectures.
Safety, Alignment, and Security Challenges: Case studies, experiments, tools to address Governance, Evaluations, Monitoring, and Observability Challenges.

Workshop Format

This full-day workshop combines keynote presentations, technical sessions, industry panels, and interactive discussions to provide a comprehensive overview of the topic. The format includes: opening keynote on governance principles, technical sessions on AI alignment and safety, industry panels sharing deployment experiences, lightning talks on technical solutions, poster sessions during breaks, interactive framework development, and collaborative roadmap development discussions. The workshop emphasizes practical outcomes through hands-on sessions and multi-stakeholder discussions that bridge the research, industry, and policy communities.

Attendance

Open to researchers, industry practitioners, policy experts, and students working on AI governance, safety, and responsible AI. Expected attendance:- 120-150 participants (or as per AAAI guidelines). We are looking forward to submissions on themes mentioned above, including novel governance challenges or practical implementation experiences.

Submission Guidelines

Submission Requirements

Submissions should be in the form of a 5-page paper (+2-page references/appendix) for proceeding articles or a 2-page abstract for posters/demonstrations. All submissions should follow AAAI formatting guidelines. Accepted papers will be presented at the workshop and included in the workshop proceedings, unless the authors opt out. The review will be double-blind, so please keep your submission(s) anonymous. The accepted papers have the option to be non-archival (i.e., only hosted on the website). The non-archival papers can be relevant work presented or published in another venue or journal.

All accepted papers and extended abstracts will be presented as posters. The program committee will select a few papers for oral presentation. There will be a poster session during the scheduled coffee breaks to facilitate and spark discussions amongst attendees. We look forward to your submissions and to seeing you at the workshop. If you have any questions, please feel free to contact the organizing committee.

Submission Site Information

Submissions via OpenReview – https://openreview.net/group?id=AAAI.org/2026/Workshop/AIGOV#tab-recent-activity

Contact: himanshujoshi@utexas.edu

Organizing Committee

Workshop Co-Chairs

Baihan Lin, Harvard University, blin@cyber.harvard.edu
Himanshu Joshi, University of Texas, Austin, himanshujoshi@utexas.edu

Organizing Committee: Baihan Lin (Harvard University), Dhari Gandhi (Vector Institute), Franziska Boenisch (CISPA Helmholtz Center), Djallel Bouneffouf (IBM Research), Himanshu Joshi (Vector Institute), Sara Migliorini (University of Macau), Lauri Goldkind (Fordham University), Sedef Akinli Kocak (Vector Institute), Shaina Raza (Vector Institute).

Program Committee: Pascal Poupart (University of Waterloo), Shaina Raza (Vector Institute), Sedef Akinli Kocak (Vector Institute), Kush Varshney (IBM Research), Francesca Rossi (IBM Research), Eddie Liywalii (Centre for AI Research, South Africa), Lucas Hartman (Vector Institute), Guarav Sharma (IIT, Delhi), Shivani Shukla (University of San Francisco), Merve Hickok (Center for AI and Digital Policy), with additional members from Microsoft Research and Google DeepMind.

Workshop Website:

AIGOV (aigovernance.github.io ) (detailed information, submission guidelines, and updates)

W27: Foretell of Future AI from Mathematical Foundation (MATH4AI)

Recent breakthroughs in artificial intelligence trace their lineage to foundational mathematical discoveries spanning centuries. The evolution of artificial intelligence (AI) from a mathematical perspective began with early foundations in mathematical logic and computation, which established the theoretical underpinnings of mechanized reasoning. Modern advancements in AI, particularly deep learning, leverage complex mathematical frameworks such as probability theory and manifold learning to handle high-dimensional data. These developments bring a large potential of AI but also raise important questions about the theoretical foundations underlying these complex architectures, making it crucial to explore the mathematical foundation of deep learning architectures. This workshop will explore how mathematical foundations can inspire deep learning in optimization, training algorithms, and architecture, and use mathematical tools, approximation theorems to evaluate new AI designs.

Topics of Interest

We encourage the paper and poster submission on the following topics to encourage the discussion on the mathematical foundation, but are not limited to:

Approximation Theorem of new architecture: Discuss how this theorem assures that neural networks can approximate any continuous function, emphasizing the implications for AI model design.
Mathematics-inspired optimization algorithms: Examining cutting-edge optimization algorithms rooted in mathematical principles.
New Architectures Inspired by Mathematical Theories: Investigating innovative AI structures based on mathematical theories.

Workshop Format

The workshop will follow a one-day format, contributed presentations from submitted papers, poster sessions, invited talks and panel discussions.

Attendance

The workshop is open to researchers, practitioners, and students who are particularly interested in the mathematical aspects of artificial intelligence. There are no specific criteria or maximum number of attendees.

Submission Guidelines

We accept both poster/short papers with no more than 4 pages and long papers with no more than 7 pages. All accepted papers will be presented as posters. The program committee will select a few papers for oral presentation.

The submissions should adhere to the AAAI paper guidelines. The contributions will be non-archival but will be hosted on our workshop website. All contributions will be peer reviewed (single-blind).

Submissions via OpenReview: https://openreview.net/group?id=AAAI.org/2026/Workshop/MATH4AI

For any questions email us at math4ai.nvaitc@gmail.com

Important Dates

Submission Deadline: October 22, 2025

Notification of Acceptance: November 5, 2025

Organizing Committee

Gitta Kutyniok, LMU Munich, kutyniok@math.lmu.de

Qianxiao Li, National University of Singapore, qianxiao@nus.edu.sg

Leevan Ling, Hong Kong Baptist University, lling@hkbu.edu.hk

Michael K. Ng, Hong Kong Baptist University, michael-ng@hkbu.edu.hk

Simon See, NVIDIA, ssee@nvidia.com

NVAITC MATH4AI: Charles Cheung, Juntao Yang, Ivan Au Yeung

Workshop Website:

https://sites.google.com/view/math4aiworkshop

W28: Post-AI Formal Methods (P-AI-FM)

As AI systems increasingly emerge through automated training, optimization, and generation, rather than direct human programming, new challenges arise to ensure their correctness, reliability, and trustworthiness. The Post-AI Formal Methods (P-AI-FM) workshop brings together researchers at the intersection of formal methods, verification, symbolic reasoning, and ML. Its goal is to foster dialogue on the challenges and opportunities posed by systems we did not fully build, but which we must still understand, trust, and reason about. Emphasis is placed on methods that enhance assurance of AI systems increasingly deployed in critical, real-world contexts such as robotics, healthcare, and natural language processing. The workshop also serves as a forum for members of the AAAI community working on formal methods for AI to exchange ideas and foster collaborations.

Relevant topics include (but are not limited to): formal verification of AI systems and correctness of AI-generated models; verification of concurrent, distributed, and multi-agent systems; automata learning, counterexample-guided synthesis, and inference; symbolic and neuro-symbolic foundations for explainability and safety; runtime monitoring and formal auditing of AI behavior; and applications of formal methods in real-world AI systems (e.g., robotics, healthcare).

Workshop Format

P-AI-FM is a one-day workshop featuring invited talks, contributed paper presentations (10 minutes each), and a panel discussion. A detailed schedule will be announced after the review process.

Attendance

Attendance is open to all registered participants of AAAI-26. To present at the workshop, authors must have a submission accepted in one of the categories below, and at least one author of each accepted submission must register for the workshop.

Submission Guidelines

We invite submissions in two categories:

1. AAAI Papers: Authors should submit the AAAI paper title and abstract, together with two separate PDF files: (i) a short explanation (1-page) of the paper’s relevance to the workshop topics; (ii) the Phase I notification (if rejected, include the reviews; if accepted, the notification alone is sufficient).

Submissions will be checked for relevance. Selected papers will be considered for oral presentations (10 minutes each).

2. Short / Position Papers (2–4 pages): Independent submissions such as vision statements, preliminary results, or open problems are warmly encouraged. Accepted short papers will be listed on the workshop website, with a link to an open-access version (e.g., arXiv), ensuring visibility within the community. While these papers will not be allocated regular oral presentation slots, depending on the number of submissions, authors may be invited to give brief “lightning talks” (2–5 minutes, informal). At least one author of each accepted short paper must register for the workshop.

Submissions via EasyChair: https://easychair.org/conferences?conf=postaifm26.

Organizing Committee

Workshop Chair:

* Noa Izsak, izsak@post.bgu.ac.il

* Andoni Rodriguez, andoni.rodriguez@imdea.org

Workshop Committee:

* Noa Izsak (Ben-Gurion University / CISPA), izsak@post.bgu.ac.il

* Andoni Rodriguez (IMDEA Software Institute), andoni.rodriguez@imdea.org

* Djordje Zikelic (Singapore Management University), dzikelic@smu.edu.sg

Workshop Website:

https://p-ai-fm.com

W29: SPARTA — Spatial Reasoning and Therapeutics with AI : From Omics to Imaging

Spatially-resolved data—encompassing spatial transcriptomics, proteomics, radiology, and digital pathology—now enable detailed mapping of molecular, cellular, and tissue-level dynamics in disease. However, integrating these heterogeneous modalities poses significant AI challenges in spatial reasoning, multi-scale modeling, and interpretable approaches for disease understanding and therapeutics research.

SPARTA brings together AI, biomedical, and industry experts to address these challenges. Our goal is to develop spatially-aware, biologically grounded AI systems capable of improving disease understanding, drug discovery, and personalized medicine. Through keynotes, peer-reviewed presentations, panels, and networking sessions, we will advance novel methods for causal inference, multimodal fusion, and therapeutic prediction in spatial data.

Topics of Interest

We invite contributions—including (but not limited to) full papers and poster presentations—in the following:

Spatial multi-omics integration
Radiology–pathology feature fusion for phenotype characterization and prediction
Digital pathology + molecular profiling for biomarker discovery.
Explainable and causal AI for multi-modal therapeutic insights.
Biological reasoning: understanding spatially-driven disease mechanisms
AI reasoning: moving beyond perception to inference and judgment
Spatio-temporal inference: capturing biological processes over time and space
Therapeutic reasoning: predicting and explaining treatment outcomes
Benchmarks & datasets for multi-modality drug discovery.
Emerging technologies such as quantum sensing/quantum imaging and applications

Workshop Format

Full-day Workshop consisting of the following activities:

Invited talks: Experts in biosensors, spatial omics, digital pathology, radiology, AI.
Peer-reviewed presentations: Full papers (8–10 pages) and posters (4 pages) following AAAI 2026 paper format
Poster and networking sessions
Panel discussion: “Bridging Sensing, AI, and Therapeutics Across Scales”

Submission Details

More information to come

Important Dates

Paper/poster submissions due: October 22th 2025
Notifications: November 5th 2025
Workshop date: January 26th or 27th 2026

Organizing Committee

Workshop Chairs:

Ilknur Icke irvi@novonordisk.com
Chao-Hui Huang chao-hui.huang@pfizer.com
Ming Tommy Tang ming.tang1@astrazeneca.com
Mahnaz Maddah maddah@broadinstitute.org

Workshop Website:

https://spartaaaai2026-workshop.github.io/SPARTA_AAAI_workshop_2026

W30: AI for Urban Planning

The 2nd Workshop on AI for Urban Planning aims to bring together researchers, practitioners, and policymakers to explore innovative AI-driven solutions for the multifaceted challenges in urban planning. The rise of new technologies and diverse data sources has produced vast, multimodal urban datasets, raising challenges related to data quality, ethical considerations, and the interpretability of AI models. This workshop will explore how AI, urban science, and planning can foster a co-learning paradigm—the “AI-Enhanced Urban Science and Planning”—to build smarter, more equitable, and sustainable cities.

Topics of Interest

We encourage submissions on a broad range of topics related to AI in urban planning, including but not limited to:

Representation and quantification of urban environment
- Representation learning for spatio-temporal dataMultimodal data fusionGraph neural networks in representing urban formsMulti-view learning
- Domain shift & generalization
Predictive modeling with urban data
- Time series forecasting
- Spatio-temporal forecasting (e.g., energy, traffic, crowd flow)
- Demographic prediction
- City climate modeling
Generative modeling, AIGC, and large models for urban planning
- Urban form design
- Land use configuration
- Architecture design
- Landscape design
- Transportation system design (e.g., road network)
Evaluation and simulation
- Reinforcement learning powered simulation
- LLM agent for evaluation and simulation (i.e., stakeholder role-play)
- Human-in-the-loop simulation
- LLM-in-the-loop simulation
Ethics of AI in urban planning
- Fairness and bias
- Community engagement
- Transparency and accountability
- Data privacy
- Interpretability
Applications, systems, and tools
- Urban digital twin
- AR, VR, and metaverse for urban planning
- Carbon neutrality
- Urban utility/resources allocation
- Planning support systems for policy maker

Workshop Format

The workshop will follow an extended full-day format, featuring keynote speeches, invited talks, paper presentations, poster sessions, and panel discussions.

Attendance

We anticipate attracting a minimum of 50 and potentially up to 100 attendees.

People who have been accepted to give paper presentations, posters are invited to attend. In addition, the workshop is open to researchers, industry professionals, and policymakers interested in AI applications for urban planning. Due to venue capacity, attendance may be limited.

Submission Guidelines

Submission Requirements

We welcome full-length papers, work-in-progress, extended abstracts, and position papers. Papers should be submitted in the AAAI format. Papers previously submitted to other journals or conferences are welcome. We accept both short papers (no more than 4 pages) and long papers (no more than 8 pages). Each submission will undergo a double-blind peer-review process.

Accepted submissions will be recommended to top journals in AI and urban planning for publication, with topics matched to the appropriate venue.

Submission Site

The workshop uses Openreview for paper submission and review. Here is the submission link: https://openreview.net/group?id=AAAI.org/2026/Workshop/AI4UP

Important Dates

Organizing Committee

Workshop Chair

Pengyang Wang (University of Macau) – pywang@um.edu.mo

Workshop Committee

Steven Jige Quan (Seoul National University) – sjquan@snu.ac.kr

Xinyue Ye (University of Alabama) – xye10@ua.edu

Hui Xiong (Hong Kong University of Science and Technology) – xionghui@hkust-gz.edu.cn

Workshop Website:

https://ai-for-urban-planning.github.io/AAAI26-workshop

W31: AI for Environment Science

This workshop explores the transformative potential of artificial intelligence (AI) in addressing critical challenges in environmental science. As climate change, biodiversity loss, and resource depletion intensify, there is an urgent need for scalable, data-driven solutions. AI methods, ranging from machine learning and deep learning to generative and foundation models, offer powerful tools for monitoring environmental indicators, forecasting ecological trends, and supporting mitigation and adaptation strategies. The workshop aims to catalyze interdisciplinary exchange, bridging AI research with environmental science, public policy, and industry to accelerate the development of impactful solutions for global sustainability.

Topics of Interest

We welcome submissions on a broad range of themes, including but not limit to:

AI for environmental modeling and forecasting (climate prediction, pollution dispersion, habitat simulation).
Machine learning for monitoring tasks such as species detection, land-use classification, and remote sensing analytics.
Multimodal approaches integrating satellite, sensor, and textual data.
Explainable and trustworthy AI for environmental science.
Generative and foundation models for environmental science.
Reinforcement learning for environmental science.
AI-powered early warning systems for natural disasters.
Ethical, fairness, and equity considerations in sustainability-focused AI applications.

Workshop Format

The workshop will be held as a half-day event, designed to balance knowledge sharing and active engagement. The program will include an opening session outlining objectives, a keynote address by a leading expert, oral presentations of accepted papers, and a panel discussion with representatives from academia, industry, and policy. A wrap-up and networking session will conclude the event, fostering long-term collaborations. Each component will include interactive Q&A, and breakout discussions will provide opportunities for in-depth dialogue. The workshop will be offered in hybrid mode to maximize inclusivity.

Attendance

The workshop is open to researchers, practitioners, and students from AI and environmental science communities.

Submission Guidelines

We welcome both short papers (up to 4 pages) and long papers (up to 7 pages) following the AAAI format, with unlimited pages for references, appendices, and the reproducibility checklist. Acknowledgements must be omitted from papers submitted for double-blind review.

See the AAAI-26 author kit for details. For more details, please refer to the submission instructions at https://aaai.org/conference/aaai/aaai-26/submission-instructions/.

Submission site: https://openreview.net/group?id=AAAI.org/2026/Workshop/AI4ES

Important Dates

Paper Submission Deadline: Friday, October 22, 2025 (AOE)
Acceptance Notification: Wednesday, November 5, 2025 (AOE)
Camera Ready: TBD
Workshop Date: Monday, January 26 – Tuesday, January 27, 2026 (AOE)

Organizing Committee

Dr. Qian Liu, University of Auckland
Dr. Di Zhao, University of Auckland
Prof. Yun Sing Koh, University of Auckland
Prof. Albert Bifet, University of Waikato

Workshop Website:

https://sites.google.com/view/ai4es/home

W32: Artificial Intelligence with Biased or Scarce Data

Despite notable advancements, integrating Artificial Intelligence (AI) into practical uses like autonomous vehicles, industrial robotics, and healthcare remains a formidable task. This complexity arises from the diverse and rare occurrences in the real world, necessitating AI algorithms to train on extensive data. However, these domains often suffer from data scarcity, making it difficult to gather raw or annotated data. Even when data is available, inherent biases creep in during collection, leading to skewed models. To address these concerns and with AI’s growing prominence, we aim to establish a platform for academics and industry professionals to deliberate on the challenges and remedies in constructing AI systems when confronted with limited data and biases.

Topics of Interest

We invite the submission of original and high-quality research papers in the topics related to biased or scarce data. The topics for AIBSD 2026 include, but are not limited to (please see the workshop website for more topics of interest):

Algorithms and theories for explainable and interpretable AI models.
Application-specific designs for explainable AI, e.g., healthcare, autonomous driving, etc.
Algorithms, theories, or performance characterization for trustworthy AI models or learning AI models under bias and/or data scarcity.
Limitation of methods incorporating large language models under bias and/or data scarcity settings.
Brave new ideas to learn AI models under bias and scarcity.

Workshop Format

This half-day workshop will include invited talks from keynote speakers, and oral/spotlight presentations of the accepted papers. Each oral presentation will be allocated between 10-15 minutes, while the spotlight presentation will be 5 minutes each. There will be live Q&A sessions at the end of each talk and oral presentation

Attendance

We expect 50~75 participants and potentially more according to our past experiences. We cordially welcome researchers, practitioners, and students from academia and industry who are interested in understanding and discussing how data scarcity and bias can be addressed in AI to participate.

Submission Guidelines

We welcome full paper submissions (up to 7 pages, excluding references or supplementary materials). The paper submissions must be in pdf format and use the AAAI official templates. All submissions must be anonymous and conform to AAAI standards for double-blind review. The accepted papers will be posted on the workshop website and will not appear in the AAAI proceedings. At least one author of each accepted submission must present the paper in person at the workshop. By submitting a paper to the AIBSD 2026 workshop for review, the authors must agree that they are willing and able to serve as the reviewers of the AIBSD 2026 workshop submissions if needed (decided by the AIBSD 2026 workshop organizing team); otherwise, their submissions are subject to desk rejection.

Organizing Committee

Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories, kp388@cornell.edu)
Abhishek Aich (NEC Laboratories, America, aaich001@ucr.edu)
Ziyan Wu (UII America, Inc., wuzy.buaa@gmail.com)

Workshop Website:

https://aibsdworkshop.github.io/2026/index.html

W33: Linguistic and Cognitive Approaches to Dialogue Agents (LaCATODA’26)

The 10th Linguistic and Cognitive Approaches to Dialogue Agents (LaCATODA’26) workshop aims to bridge the persistent gap between current “chatbots” and truly natural, human-like conversational AI. While Generative AI has demonstrated remarkable fluency, significant challenges remain in achieving genuine dialogue understanding, nuanced interaction, and cognitive grounding. This workshop will discuss the multidisciplinary issues crucial for developing sophisticated dialogue agents that move beyond mere information exchange to truly engage with human users. By exploring the intricate interplay of linguistics, cognitive science, and computational approaches, we hope to foster a deeper understanding of human communication. The workshop is specifically designed to address the critical new challenges and opportunities presented by the rapid evolution of Large Language Models (LLMs) in dialogue systems. Although LLMs have dramatically advanced the fluency and coherence of machine-generated text, their inherent limitations in achieving true human-level conversational intelligence have become increasingly apparent in 2025. Our goal is to push the boundaries beyond probabilistic text generation and target the specific issues that underpin genuinely natural, intelligent, and human-centric dialogue. 

Topics of Interest

Adaptive Dialogue Management,
Cognitive Gaps in LLMs,
Common Sense Reasoning,
Complex Dialogue Systems,
Embodied and Situated Dialogue,
Ethical AI and Trustworthy Dialogue,
Humor Processing,
Nuance of Affect and Intent,
Persona Modeling,
Proactive Dialogue Management,
RAG (and beyond)-based Dialogue

Workshop Format

We plan for a half day workshop comprising:

Invited talk by established expert in the field 
Full paper presentations
A panel discussion on issues and pitfalls of current trends (if time allows)
If the number of accepted submissions is big, a poster session will be held

Attendance

We anticipate an audience of approximately 100 researchers and industry representatives who are interested in our topics. Our workshop is specifically designed to bring together a multidisciplinary community of researchers from linguistics, cognitive science, and computational approaches. We aim to attract attendees who are keen to move beyond current “chatbots” toward truly natural, human-like conversational AI.

Submission Guidelines

6-12 pages for full papers; 
4-5 pages for short/position papers
Submission Site Information: https://cmt3.research.microsoft.com/LaCATODA2026

Organizing Committee

Workshop Chair: Rafal Rzepka (rzepka@ist.hokudai.ac.jp)
Rafal Rzepka, Hokkaido University, rzepka@st.hokudai.ac.jp
Michal Ptaszynski,  Kitami Institute of Technology, michal@mail.kitami-it.ac.jp
Pawel Dybala, Jagiellonian University, pawel.dybala@uj.edu.pl

Workshop Website:

http://sites.google.com/view/lacatoda2026/

W34: Navigating the Model Uncertainty and the Rashomon Effect: From Theory and Tools to Applications and Impact

In many real-world AI problems, there is not a single “best” model but rather a multiplicity of models that achieve comparable performance. This phenomenon, known as the Rashomon Effect, raises fundamental questions about uncertainty, diversity, and model choice. Understanding and leveraging model multiplicity has become crucial for building trustworthy, reliable, and responsible AI systems.

This half-day workshop will bring together researchers and practitioners to explore the theoretical foundations, algorithmic methods, and societal implications of predictive multiplicity. We will highlight both classical approaches and emerging challenges in the era of generative models. A special emphasis will be placed on providing opportunities for junior researchers to present their work and view on the field and engage in open discussion, helping to grow a vibrant community.

Topics of Interest

We welcome contributions about the Rashomon Effect that include:

Position papers offering perspectives on the role of multiplicity in AI practice, including deployment, governance, and policy.
Connections between the Rashomon Effect and generative models.
Applications and impacts of predictive multiplicity in deployed systems.
Connections between the Rashomon Effect and (i) uncertainty, (ii) explainability, (iii) fairness, (iv) privacy, and (v) robustness.
Theoretical and algorithmic approaches for understanding, constructing, auditing, and navigating Rashomon sets.

Workshop Format

The workshop will run as a half-day event, featuring invited talks, an interactive discussion, poster and spotlight sessions. In addition, we will host a “Perspectives from the Community” session, where participants are welcome to share overviews of their work, perspectives on the field, or reflections on model multiplicity. We particularly encourage contributions from junior researchers, including those without a formal paper submission (further details are available on the workshop website).

Attendance

The workshop is open to all AAAI-26 attendees. Presenters will be invited from accepted submissions, and we encourage participation from researchers, students, and practitioners interested in trustworthy AI.

Submission Guidelines

We invite submissions of 4-6 pages (plus unlimited references and appendices). Submissions should be anonymized, follow the official template and will be handled via OpenReview, with double-blind reviewing. Three tracks are offered:

Main track: new research contributions (including early-stage ideas).
Published paper track: recently published or accepted work for presentation.
Position paper track: commentary of broader interest on the current state of model multiplicity.

This workshop is non-archival. Accepted papers will be available on the workshop website.

Important Dates

Submission Opens: September 15, 2025
Submission Deadline: October 15, 2025
Notification to Authors: November 5, 2025

Organizing Committee

Lesia Semenova (Rutgers University, lesia.semenova@rutgers.edu)
Chudi Zhong (University of North Carolina at Chapel Hill, chudi@unc.edu)
Varun Babbar (Duke University, vb97@duke.edu)
Hayden McTavish (Duke University, hayden.mctavish@duke.edu )
Lucas Monteiro Paes (Harvard University/Apple MLR, lucaspaes@g.harvard.edu )

Workshop Website:

https://sites.google.com/view/rashomon-aaai-workshop

W35: Language Models for Underserved Communities(LM4UC)

We are excited to announce the 2nd Language Models for Underserved Communities (LM4UC) (Website: https://lm4uc.github.io) workshop, co-located with AAAI 2026. The workshop will take place in person and virtually on January 26, 2026, in Singapore. LM4UC invites researchers, practitioners, and policymakers to address challenges and propose innovative solutions for building and deploying language models (LMs) for underserved languages and communities.

Underserved communities often lack adequate access to advanced NLP technologies due to limited linguistic data, insufficient computational resources, or inadequate AI governance frameworks. This gap hinders equitable access to NLP advancements, exacerbating the digital divide. Our workshop aims to address this by fostering a multidisciplinary dialogue around the development of LMs that prioritize cultural sensitivity, resource efficiency, and sustainable AI practices.

Topics of Interest

We invite submissions of full papers, ongoing work, position papers, and survey papers on topics including, but not limited to:

Measuring and Governing AI: Developing reliable evaluation methods for LMs under constraints in data, compute, and expertise. How can psychometrics, auditing frameworks, or validity theory guide responsible measurement and governance?
Benchmarking and Fairness: Building inclusive benchmarks and evaluation pipelines that reduce bias, improve cultural and linguistic representation, and ensure fair performance across underserved communities.
Pluralistic Alignment: Designing approaches for aligning LMs with diverse values, cultural norms, and epistemologies, including participatory and community-driven methods.
Open and Inclusive Infrastructure: Creating open datasets, benchmarks, models, and participatory platforms that support sustainable and equitable NLP research and deployment.

Workshop Format

We welcome long papers (8 pages) and short papers (4 pages), excluding references. Submissions must follow the AAAI 2026 style guidelines.

Submission Guidelines

We welcome long papers (8 pages) and short papers (4 pages), excluding references. Submissions must follow the AAAI 2026 style guidelines.

Important Dates

Submission deadline: November 14, 2025
Notification of acceptance: December 12, 2025
Camera-ready paper due: January 10, 2026
Workshop dates: January 27, 2026

Workshop Website:

NAACL Workshop: Language Models for Underserved Communities

W36: LLM-based Multi-Agent Systems: Towards Responsible, Reliable, and Scalable Agentic Systems (LaMAS 2026)

This workshop focuses on the emerging field of multi-agent systems powered by LLMs (LaMAS). As interest in using multiple LLM agents to solve complex problems grows, our main objective is to systematically address the critical challenges and opportunities that arise from their interaction. We aim to bridge the gap in understanding failure modes, alignment challenges, and responsible behavior in these systems. The workshop will foster discussion on making these systems powerful, transparent, verifiable, and aligned with human intent.

Topics of Interest

Organization: Interaction paradigms, coordination strategies, and communication protocols for multiple LLM agents.
Evaluation and Optimization: Assessing the performance of LaMAS and techniques for enhancing their efficiency and effectiveness.
Safety, Trust, and Responsibility: Designing safe, aligned, and trustworthy LaMAS, including responsible agent behavior, human-in-the-loop oversight, and regulatory frameworks.
Real-World Applications: Deployments in fields like software development, scientific research, and education, as well as infrastructure for large-scale agentic systems.

Workshop Format

LaMAS 2026 will be a one-day workshop. The format is designed to be highly interactive, featuring presentations, discussions, and networking opportunities. The day will include: 4 invited talks, 2 poster sessions, a panel discussion and Oral presentations for about ten contributed papers.

Attendance

Attendance is open to all researchers and practitioners interested in the field.

Submission Guidelines

We welcome both short papers (up to 4 pages) and long papers (up to 8 pages) following the AAAI format. Submissions may include recently published work, under-review papers, work in progress, and position papers. All submissions will undergo peer review through a double-blind process. While workshop publication is non-archival, accepted papers will be featured on our website with the author’s permission.

Submission Site Information: Please look at https://sites.google.com/view/lamas2026

Organizing Committee

Workshop Chairs:

Mining Wen (contact person), Shanghai Jiao Tong University, muningwen@sjtu.edu.cn
Shuang Ao (contact person), University of Southampton, s.ao@soton.ac.uk
Stefano V. Albrecht, DeepFlow London, ste.albre@gmail.com
Sarvapali D. (Gopal) Ramchurn, University of Southampton, sdr1@soton.ac.uk
Ramayya Krishnan, Carnegie Mellon University, rk2x@cmu.edu

Workshop Committee:

Weinan Zhang, Shanghai Jiao Tong University, wnzhang@sjtu.edu.cn
Yi Dong, University of Liverpool, Yi.Dong@liverpool.ac.uk
Arrasy Rahman, The University of Texas at Austin, arrasy@cs.utexas.edu
Peter Stone, The University of Texas at Austin, pstone@cs.utexas.edu

Workshop Website:

https://sites.google.com/view/lamas2026

W37: Machine Ethics: from formal methods to emergent machine ethics

Machine ethics is concerned with the behaviour of machines towards people and other machines. Advances in Artificial Intelligence (AI) continue to reinforce the need for research work in this field either in the top down, bottom up or hybrid approaches. In one direction this work involves specifying, implementing, and verifying ethical and safe AI. In another direction is the study of ethics that emerges by self-organizing inside AI-centric societies. We need to be able to verify the existing ethical capabilities of AI systems, and make advances towards understanding how transparent, collaboratively guidable AI ecosystems might evolve. All of these efforts, while happening at different technology and conceptual levels, are united in the need to establish a global hub for AI-safety, multi-agent cooperation, and governance research.

Twenty years ago, a fall AAAI Fall symposium on Machine Ethics, kick-started the machine ethics field in computer science (https://auld.aaai.org/Library/Symposia/Fall/fs05-06.php ). The time is right for a formative venue of a new generation of machine ethics researchers and teams. Building on emerging work in communities such as Japan’s SIG-AGI ( https://www.sig-agi.org/sig-agi/event/sig-agi-30-panel-en ), where foundations for Emergent Machine Ethics are being actively developed, this workshop marks a critical evolution from traditional top-down approaches to include bottom-up emergence, recognizing that as AI systems become increasingly autonomous, we need both formal verification methods and emergent approaches working in tandem to respect their agency while ensuring beneficial outcomes.This workshop has two objectives.

Consolidation: investigation of formal methods for and in machine ethics and value alignment.
We aim to provide an arena for presenting new work on classical topics studied in machine ethics from an artificial intelligence and computer science perspective. We hope to see and discuss to tackling challenges of how autonomous and intelligent systems
systems can acquire verifiably correct behavior: how we can specify, implement and validate ethical and safe reasoning.
Emergence: investigation of Emergent Machine Ethics (EME) – ethics that self-organize within AI-centric societies. As AI capabilities advance exponentially, the rapid pace of development, dynamic human values, and emergent AI-AI interactions necessitate ethics that develop internally within AI systems to complement traditional approaches. EME encompasses studying ethical emergence dynamics, developing inter-intelligence evaluation systems, and facilitating human-AI co-creative guidance. Through this paradigm, we seek to understand how AI societies might develop internal ethical capabilities that scale with their increasing autonomy and complexity.

Topics of Interest

The topics of this workshop include but are not limited to:

Logics for morality and ethics
Knowledge representation of ethical theories and ethically salient information
Computational modeling of morality and ethics
Specification of ethical reasoning and behavior
Verification of ethical reasoning and behavior
Formal modeling of ethical accountability item Formal modeling of agent responsibility
Normative reasoning, concepts, and systems in relation to moral behavior of artificial agents
Neuro-symbolic approaches towards attaining ethical behavior in artificial agents and autonomous systems
Ethical reinforcement learning agents
Runtime monitoring of ethical issues
Dynamic, and formally justified, ethical repair
Emergent Machine Ethics (EME) – foundational theories and frameworks
Comparative life-form analysis for value-neutral ethics exploration
Individual attachment differences between humans and digital entities
Ethics emergence dynamics in multi-agent AI systems
Autonomous norm formation, internalization, and stabilization
Emergent cooperation without external enforcement
Self-organizing governance in AI ecosystems
Convergence criteria for collaborative ethical systems
Inter-intelligence evaluation and mutual assessment frameworks
Human-AI co-creative guidance mechanisms
Bidirectional ethical influence in human-AI societies
Value alignment through internal emergence rather than external control
Scalable approaches to internal ethical development
Benchmarks and metrics for emergent ethics

Workshop Format

This is a one day workshop. The workshop will be organized as a single track with multiple thematic blocks, including dedicated sessions for formal methods and emergent machine ethics (EME), culminating in joint panel discussions that explore synergies and productive tensions between the two approaches.

Attendance

the workshop is open to all interested participants of AAAI.
Submission requirements: full papers and short abstracts

Submission Guidelines

Full: A full paper would follow the same format as the AAAI submissions (https://aaai.org/conference/aaai/aaai-26/submission-instructions/).

Fast review track: papers rejected from AAAI could be resubmitted with reviews and a cover letter on how the issues were addressed.

Short: 4 pages extended abstract (same template as https://aaai.org/conference/aaai/aaai-26/submission-instructions/) that puts forward work that invites discussion but is not yet mature for publication.

Important Dates

Submission deadline: November 10, 2026 AOE
Notification: November 25, 2026 AOE

Organizing Committee

Workshop Chairs:

Louise Dennis
Taichiro Endo
Michael Fisher
Ryutaro Ichise
Raynaldo Limarga
Hiroshi Yamakawa
Rafal Rzepka {contact}
Marija Slavkovik {contact}

Workshop Website:

https://www.aialign.net/ws-machine-ethics

W38: Machine Learning for Wireless Communication and Networks (ML4Wireless)

This workshop aims to explore the future of AI-native wireless communication and networks, where advances in communication, machine learning (ML), and signal processing theories intersect with new architectures and applications. Key themes include semantic communication, where meaning rather than raw bits is transmitted to boost efficiency. Generative and language models compress, interpolate, and regenerate signals for high-stakes applications such as robotics and immersive media. AI-based radio access networks (AI-RAN) integrate RAN functions and AI applications on shared GPU-driven infrastructures. Token-based communication enables intelligent agents to interact through tokens as universal representations. The workshop emphasizes grounding these concepts through both theory and practice. Information bottleneck principles and inverse problem formulations support semantically aligned multimedia reconstruction. Statistical physics and approximate Bayesian inference offer tools for context-preserving sampling in token-based generative models. Optimization techniques, game-theoretic frameworks, and agentic coordination methods guide the orchestration of AI-RAN systems. By linking rigorous theoretical analyses with empirical studies, the workshop seeks to foster cross-disciplinary discussion between ML researchers and wireless experts and promotes innovation toward scalable, efficient, and trustworthy intelligent communication systems.

Topics of Interest

Distributed AI/ML over wireless networks
Split, hybrid, and collaborative inference in resource-constrained environments
Neural compression and transmission of multimedia and multimodal data
Generative and agentic AI for autonomous and coordinated decision-makings
Agentic AI communication protocols over wireless networks
AI/ML for and on Radio Access Network (RAN)
Lightweight on-device neural network architectures for wireless communications
Low-latency adaptation, quantization, and test-time compute for wireless communications
Privacy, security, and trustworthy AI-enabled wireless services
Explainability, reliability, and efficiency in semantic and token-based communications
Theoretical foundations and performance bounds of AI-native wireless systems
High-fidelity synthetic data and simulation frameworks for network intelligence
Practical validations and testbeds for AI-native communication and networks

Workshop Format

It will be a one-day workshop, including keynote and invited talks, paper presentations, and a panel discussion.

Attendance

Invited speakers and attendees may include ML researchers in scalable, distributed, and coordinated inference, decision-making, and fine-tuning, and communication researchers exploring generative, language model-based, and agentic AI solutions, as well as industry experts from hyperscalers, RAN vendors and operators, and on-device AI chipset manufacturers.

Submission Guidelines

Submission Requirements

We welcome two types of papers:

Technical papers: max. 7 pages, excluding references and appendices
Short or position papers: max. 4 pages

All papers must be submitted in PDF format, following the AAAI 2026 format. Papers will be peer-reviewed (single-blind) and selected for oral and/or poster presentation at the workshop. The contributions will be non-archival but will be hosted on our workshop website.

Submission Site

The submission will be conducted through OpenReview. Submission site/URL will be announced later in our workshop website (https://sites.google.com/view/ml4wirelessaaai26/home).

Important Dates

Organizing Committee

Workshop Chair

Jihong Park, Singapore University of Technology and Design, Singapore, jihong_park@sutd.edu.sg
Zihan Chen, Singapore University of Technology and Design, Singapore, zihan_chen@sutd.edu.sg
Eleonora Grassucci, Sapienza University of Rome, Italy, eleonora.grassucci@uniroma1.it
Xueyan Niu, Huawei Technologies, China niuxueyan@gmail.com

Workshop Committee:

Workshop Chair
Jihong Park, Singapore University of Technology and Design, Singapore, jihong_park@sutd.edu.sg
Zihan Chen, Singapore University of Technology and Design, Singapore, zihan_chen@sutd.edu.sg
Eleonora Grassucci, Sapienza University of Rome, Italy, eleonora.grassucci@uniroma1.it
Xueyan Niu, Huawei Technologies, China niuxueyan@gmail.com

Workshop Committee:

Final PC member list is to be determined and updated on the workshop website.

Workshop Website:

https://sites.google.com/view/ml4wirelessaaai26

W39: Neuro for AI & AI for Neuro: Towards Multi-Modal Natural Intelligence

This workshop will unite researchers in artificial intelligence, computational neuroscience, and neuromorphic engineering to explore the critical two-way exchange between biology and machine learning. Our main objective is to bridge these fields to accelerate foundational progress in multimodal natural intelligence. The “Neuro → AI” theme will investigate how principles from cortical microcircuits, such as sparse coding, dendritic nonlinearities, and cell-type diversity, can inspire novel, efficient, and robust AI architectures like transformers and graph-based models. Conversely, the “AI → Neuro” theme will showcase how advanced machine-learning techniques are revolutionizing neuroscience, enabling new insights into neural dynamics through AI-driven analysis of large-scale imaging, electrophysiological, and behavioral data. By bridging the gap between biologically grounded inductive biases for AI and scalable computational tools for neuroscience, this workshop aims to catalyze research that unifies theory, experiment, and application.

Topics of Interest

Biologically inspired machine learning architectures
Neuro-inspired mechanisms for efficiency (e.g., sparsity, inhibitory-excitatory balance)
AI-driven analysis of large-scale neuroscience data
Unsupervised representation learning and causal inference for circuit discovery
Gradient descent-based training of bio-realistic neural models
Open-source software and reproducible research in NeuroAI
Neuromorphic engineering and hardware implementation

Workshop Format

This will be a one-day, in-person workshop. The format is designed to be highly interactive, featuring a series of invited talks from world-renowned experts, two panel discussions for in-depth Q&A, and spotlight presentations for high-impact contributed papers. A central poster session will provide a forum for detailed discussion and networking. The day will conclude with a roundtable discussion to identify key community challenges and a sponsored social gathering to foster continued collaboration.

Attendance

We welcome researchers and practitioners from academia and industry with an interest in the intersection of neuroscience and artificial intelligence. At least one author of each accepted submission must be present at the workshop. The maximum number of attendees is to be determined by the room size and will be communicated by AAAI.

Submission Guidelines

We invite submissions of full papers or abstracts that describe new research, work in progress, or position statements on relevant topics. The full paper submissions should be 8 pages maximum, excluding references, and the abstract submissions should be 2 pages maximum, excluding references, in the AAAI two-column format.

Submission Site Information: The submission will be conducted through OpenReview using the following link: https://openreview.net/group?id=AAAI.org/2026/Workshop/NeuroAI

Important Dates

Paper Submission: October 30, 2025

Notifications: November 15, 2025

Early Registration: November 16, 2025

Standard Registration: December 14, 2025

Late Registration: After December 14, 2025

Organizing Committee

Reza Abbasi-Asl, University of California, San Francisco (Email: Reza.AbbasiAsl@ucsf.edu)
Asim Iqbal, Tibbling Technologies / Weill Cornell Medicine (asim@tibbtech.com)
Sophia Sanborn, Stanford University (sanborn@stanford.edu)
Anton Arkhipov, Allen Institute (antona@alleninstitute.org)
Shinya Ito, Allen Institute (shinya.ito@alleninstitute.org)

Workshop Website:

https://neuroai-multimodal-workshop.github.io

W40: Neuromorphic Intelligence: From Algorithms to Systems

This workshop aims to highlight interdisciplinary advances in neuromorphic intelligence, from software algorithms to hardware systems and their synergistic co-design. It is inspired by the brain’s efficient and parallel processing capabilities, which provide a compelling paradigm shift to address limitations of conventional deep learning in energy efficiency, real-time processing, and adaptability within dynamic environments.

Topics of Interest

Neuromorphic Sensing: Neuromorphic sensors (event sensors, spike sensors), data representation, simulation, processing, and applications (in Computer Vision, Audio, etc.).
Neuromorphic Networks: Spiking Neural Networks (SNNs), Neuromorphic learning algorithms, Neuromorphic temporal processing.
Algorithm-Hardware Co-design: Interplay between neuromorphic algorithms and their underlying hardware architecture
Benchmarking: Establish standardized neuromorphic methodologies and datasets to evaluate the performance, efficiency, and robustness of neuromorphic systems.

Workshop Format

This is a one-day workshop featuring a mix of invited talks, paper presentations, and a panel session. The workshop is organized around specific topics, fostering a deep dive into the core research challenges and advancements in neuromorphic intelligence.

Attendance

We invite researchers, practitioners, and students from both academia and industry to join us in exploring and discussing neuromorphic intelligence. Shape

Submission Guidelines

We invite submissions that present theoretical advancements, practical applications, and experimental results in the field of brain-inspired, energy-efficient, and real-time AI.

Submissions should be formatted using the AAAI template. Long papers are limited to a maximum of 8 pages, while short papers are limited to a maximum of 4 pages, both excluding references.

Please note that this workshop does not have formal proceedings.

Submission Site Information

Please submit your papers to the following OpenReview website: https://openreview.net/group?id=AAAI.org/2026/Workshop/NI

Important Dates

Organizing Committee

Dr. Yueyi Zhang, yueyi.zhang@miromind.ai, MiroMind
Dr. Zongwei Wu, zongwei.wu@uni-wuerzburg.de, University of Würzburg
Prof. Lin Wang, linwang@ntu.edu.sg, Nanyang Technological University
Prof. Zhiwei Xiong, zwxiong@ustc.edu.cn, University of Science and Technology of China
Prof. Pascal Vasseur, pascal.vasseur@u-picardie.fr, University of Picardie Jules Verne

Workshop Website:

https://neurointelworkshop.github.io/aaai26

W41: New Frontiers in Information Retrieval

This workshop will advance the frontier of information retrieval (IR) by focusing on systems that can reason over complex queries, follow tailored instructions, and operate robustly across domain-specific and multimodal content. With rapid progress in foundation models, ranking and reranking are being reimagined around rich semantics, long-term memory, and agentic workflows. We aim to bring together researchers and practitioners to exchange insights on challenges, architectures, data curation, training strategies, and Retrieval-Augmented Generation paradigms that will shape the next generation of intelligent IR systems.

Topics of Interest

Topics include, but are not limited to:

New IR Challenges
Reasoning-intensive queries and instruction-following retrieval
Domain-specific retrieval across scientific and enterprise settings
Multimodal retrieval that mixes text, image, and other signals
Cached vector retrieval to accelerate LLM inference
Memory retrieval to maintain long-term coherence for agent systems
New IR Models
Ranking and reranking architectures built on modern foundation models
Transitions from BERT-style encoders to large language models and diffusion-based language models
Multimodal modeling progress from CLIP to multimodal LLMs and multimodal diffusion models, and how these enable richer scoring functions
New Data and Training Methods
Data augmentation to elicit new capabilities such as reasoning and instruction-following
Data quality assessment, cleaning, and techniques for data efficiency with minimal supervision
Multi-stage training pipelines, smart-batching, and hard-negative passage mining
Meta-learning and curriculum learning strategies tailored to retrieval tasks
New Retrieval-Augmented Generation paradigms
Agentic retrieval with explicit reasoning and planning
Query understanding and reformulation, and routing to appropriate data sources
Interleaving tool use with search and processing of retrieved evidence to improve response generation

Workshop Format

The workshop will be a one-day workshop, focusing on paper presentations, poster sessions, panels, and invited talks.

Attendance

All AAAI attendees who are interested in Information Retrieval are welcome.

Submission Guidelines

We welcome two types of papers:

Full papers: full-length research papers from 4 to 7 pages (excluding references and appendices).
Short papers: research/position papers of up to 4 pages (excluding references and appendices).

The workshop uses Openreview for paper submission and review. The submission link is https://openreview.net/group?id=AAAI.org/2026/Workshop/FrontierIR.

Important Dates

Submission Deadline: October 22, 2024
Notification of Acceptance: November 5, 2025

Organizing Committee

Chen Zhao, NYU Shanghai, cz1285@nyu.edu
Yilun Zhao, Yale University, yilun.zhao@yale.edu
Siyue Zhang, Nanyang Technological University, siyue001@e.ntu.edu.sg
Anh Tuan Luu, Nanyang Technological University, anhtuan.luu@ntu.edu.sg
Arman Cohan, Yale University, arman.cohan@yale.edu
Bryan Hooi, National University of Singapore, bhooi@comp.nus.edu.sg
Emine Yilmaz, University College London, emine.yilmaz@ucl.ac.uk

Workshop Website:

https://frontier-ir-workshop.github.io

W42: Next-Gen Code Development with Collaborative AI Agents Workshop

The Next-Gen Code Development with Collaborative AI Agents workshop will explore the opportunities and challenges of building AI teammates that work alongside human developers in modern software engineering. Unlike traditional code completion tools, collaborative AI agents must coordinate planning, implementation, debugging, testing, and documentation — both with each other and with humans — to solve complex software engineering tasks.

The workshop will bring together researchers and practitioners from AI, software engineering, programming languages, and HCI to discuss architectures, interaction models, evaluation methodologies, and deployment frameworks for AI teammates in code development. Participants will engage through invited talks, accepted paper presentations, and interactive sessions, fostering collaboration across academia and industry.

Topics of Interest

Topics include, but are not limited to:

Collaborative AI architectures for software development
Multi-agent coordination strategies
Human–AI workflow integration and handoff mechanisms
Preserving human expertise and agency in AI-assisted coding
Interaction design in IDEs, CLIs, and collaborative environments
Trust, reliability, and safety in collaborative coding agents
Verification, validation, and auditing frameworks
Evaluation methodologies and benchmarks for collaboration
User studies and empirical developer experience evaluations
Industrial deployments and case studies of AI coding agents
Applications in planning, implementation, debugging, and testing
Lessons from systems such as GitHub Copilot, Amazon Kiro, Cursor AI, and Asimov

Workshop Format

AAAI conference style

Submission Guidelines

Submission Site: https://cmt3.research.microsoft.com/CodeMates2026

Important Dates

(all deadlines are AoE, subject to AAAI final workshop schedule)

September 20, 2025: Submissions open
October 22, 2025: Paper submission deadline
December 5, 2025: Author notification
January 20–27, 2026: Workshop at AAAI-26 (Singapore EXPO)

Organizing Committee

Shweta Garg (AWS AI Labs, Santa Clara)
Behrooz Omidvar-Tehrani (Microsoft, Mountain View)
Sewon Min (UC Berkeley, Allen Institute for AI)
Baishakhi Ray (Columbia University, New York City)

Workshop Website:

https://sites.google.com/view/next-gen-code-agents-aaai26

W43: Orchestrating Synthesized Human and AI-Agentic Workflows: AI Agency Benefits, Disruptions and Management

Our interests are synthesizing Generative AI (Gen-AI) and humans into intelligent interdependent human, machine, AI, or agentic AI teams. Once synthesized into a team, Gen-AI provides the opportunity to not only advance the science of teams by orchestrating team products and results, but also has raised several concerns (e.g., AI used for deception, superintelligence, existential threats). For Agentic AI, what are its benefits, contexts, drawbacks, and even whether humans, machines and Agentic AI can be synthesized and orchestrated?

Topics of Interest

Topics include, but are not limited to:

Prediction, control, conflict, competition and safety of orchestrating team workflows
The benefits, advantages, awareness needs, and drawbacks of Agentic AI
The synthesis, context factors and settings for AI-human-machine team workflows
The orchestration of AI-human-machine team workflows
A library of bidirectional agency for operational workflows
Agentic risk tolerances across skills and task workflows
Transparency, traceability, and explainability of agentic outputs
Architectures of structures, strategies and concerns for collaborative agent-based systems
Interdependence, cooperation, bidirectional trust issues, and sizes of teams
Risk mitigation of human-machine-AI tasks and workflows
Team operation timelines, agency and task assignments

Workshop Format

AAAI conference style (2-3 pages; please delete copyright): https://aaai.org/wp-content/uploads/2025/07/AuthorKit26-1.zip

Submission Guidelines (submissions to organizers or easychair):

OSHAIAW-26 workshop: https://easychair.org/conferences2/overview?a=35113396

Important Dates

September 22, 2025: Submissions open
October 22, 2025: Paper submissions to Organizers or Easychair by deadline
November 5, 2025: Notifications sent to authors
Sunday, November 16, 2025: AAAI-26 Early Registration Deadline (11:59 EST)
January 27, 2026 (Tuesday): Workshop #43 at AAAI-26 (Singapore EXPO, Singapore)

Organizing Committee

William Lawless (w.lawless@icloud.com; Paine College, Augusta, GA)

Ranjeev Mittu(ranjeev.mittu.civ@us.navy.mil; Info. Tech. Dpt., Naval Research Lab., DC)

Shannon P.D. McGarry(shannon.p.mcgarry.civ@us.navy.mil; ITD, Naval Res. Lab., DC)

Marco Brambilla (marco.brambilla@polimi.it; Politecnico di Milano, Italy)

Program Committee:

Kristin E. Schaefer-Lay, kristin.e.schaefer-lay.civ@army.mil, DEVCOM ARL

J. Llinas, llinas@buffalo.edu, U at Buffalo

Erik Blasch, erik.blasch@gmail.com, AFRL, AFOSR

Michael Wollowski, wollowsk@rose-hulman.edu, Rose-Hulman Inst.; AI Mag. Assoc. Ed.

Workshop Website:

https://sites.google.com/view/aaai-26-agentic-workflow/home

W44: Personalization in the Era of Large Foundation Models

While foundation models excel across NLP, computer vision, and multimodal tasks, they cannot capture individual user characteristics—preferences, behavioral patterns, and contextual needs—creating a disconnect between general intelligence and personalized user experience. This workshop “Personalization in the Era of Large Foundation Models (PerFM 2026)” will unite researchers and practitioners to explore theoretical foundations, scalable architectures, evaluation methods, lifelong learning, and ethical considerations, shaping the next generation of AI systems that adapt to and grow with individual users.

Topics of Interest

The workshop focuses on the intersection of foundation models and human-centered AI, covering. Topics include, but are not limited to:

Theoretical foundations for personalization and privacy in large foundation models
Evaluation frameworks and benchmarks to measure personalization effectiveness across various tasks, modalities, and demographic groups
Novel architectural approaches for incorporating user-specific information across modalities (parameter-efficient fine-tuning, adapters, prompt-based strategies)
Efficient personalization architectures that scale to millions of users with lightweight deployment and cold-start solutions
Memory and lifelong learning systems that balance short-term context with long-term user understanding
Privacy, ethical, and social considerations including algorithmic fairness, transparency, and data protection policies
Cross-domain applications spanning healthcare, education, scientific research, creative industries, and enterprise solutions

Workshop Format

The workshop will be a full-day workshop with a mix of keynotes, contributed talks, focused discussions, and poster sessions. 

Submission Guidelines

More information to come

Important Dates

More information to come

Organizing Committee

Jiahong Liu: The Chinese University of Hong Kong, jiahong.liu21@gmail.com
Yang Zhang: National University of Singapore, zyang1580@gmail.com
Weizhi Zhang: University of Illinois Chicago, wzhan42@uic.edu
Runcong Zhao: King’s College London, runcong.zhao@kcl.ac.uk
Lucas Vinh Tran: JPMorganChase, lucasvinhtran@gmail.com

Workshop Website:

https://personalizedllm.github.io/events/perfm-aaai26

W45: Foundations of Agentic Systems Theory (FAST)

As with any complex system, the most interesting and consequential behaviors often arise not from the parts in isolation, but from the patterns of interaction between them. The current development of agentic AI has largely ignored these considerations, instead focusing on designing more (individually) capable agents. Failing to consider these effects as AI agents become more widespread will lead to a significant underestimation in both their capabilities and risks.

The Foundations of Agentic Systems Theory (FAST) workshop broadly aims to help evaluate the degree to which existing theory can be used to describe the behavior of agentic AI systems. Drawing from a variety of fields (notably beyond computer science, including developmental psychology, neuroscience, and social dynamics), FAST will explore both if and how existing mechanisms of emergent behavior from other systems carry over to systems of LLM-based agents, the properties of the underlying agents (and their LLMs) that facilitate these behaviors, and our ability to control/induce desirable system-wide outcomes. We strongly seek interdisciplinary participation (via both contributions and invited talks), with the ultimate goal of fundamentally contributing to a better understanding of the underlying processes that govern the system-level behavior (and risks) of agentic AI systems.

Topics of Interest

Mechanisms of emergent capabilities (in both biological and artificial agents)
Evaluation, detection, and mitigation/bounding of emergent capabilities in AI systems
Incentive mechanisms for inducing behavior in systems of LLM-based agents
Definitions of agency (of agents, and of systems); philosophy of agency in artificial systems
Definitions of emergence in engineered systems
Benchmarks and datasets for monitoring capabilities/agency, risks, and failure modes in agentic AI systems

Workshop Format

FAST will be a 1-day workshop featuring invited talks, poster sessions, lightning talks, and a panel discussion. The program is designed to encourage idea exchange and collaboration, with ample time for interactive poster presentations, short lightning talks highlighting impactful work, and a panel exploring methods, emergence, and risks in translating mechanisms of emergence to artificial systems. Social breaks throughout the day will provide additional opportunities for networking and discussion.

Attendance

We anticipate approximately 50–60 participants. Attendance will include authors of accepted posters and lightning talks, as well as invited speakers. In addition, the workshop is open to researchers and practitioners with interests aligned with the themes of FAST. Due to capacity limits, participation may be restricted to ensure an engaging and interactive environment.

Submission Guidelines

We invite both full and short paper submissions.

Full papers: Up to 7 pages (excluding references and appendices); should present mature or completed research.
Short papers: Up to 4 pages (excluding references and appendices); intended for describing ongoing work, early-stage ideas, or the release of benchmarks and datasets (authors are encouraged to use the short paper format for benchmarks and datasets).

Submission Site Information:

All submissions must be made through our OpenReview page (https://openreview.net/group?id=AAAI.org/2026/Workshop/FAST). Please follow the AAAI template when preparing your submission. Submissions must be anonymized for double-blind review. Reviewing will follow the standards of AAAI, with evaluation based on novelty, technical depth, clarity, reproducibility, and potential impact. Accepted papers will be presented as either posters or lightning talks. At least one author of each accepted paper must register and attend the workshop.

Important Dates

September 12, 2025: Submission window opens (OpenReview)

October 19, 2025: Abstract submission deadline

October 22, 2025: Paper submission deadline

November 5, 2025: Acceptance notification

January 27, 2026: Workshop

Organizing Committee

Workshop Chair:
Erik Miehling, erik.miehling@ibm.com

Workshop Committee:

Erik Miehling, IBM Research – Ireland, erik.miehling@ibm.com
Chenchen Ye, UCLA, ccye@cs.ucla.edu
Atoosa Kasirzadeh, Carnegie Mellon University, atoosa@cmu.edu
Djallel Bouneffouf, IBM Research – Yorktown Heights, djallel.bouneffouf@ibm.com
Anne Arzberger, TU Delft, a.arzberger@tudelft.nl

Workshop Website:

https://fast-workshop.github.io

W46: Quantum Computing and Artificial Intelligence (QC+AI 2026)

Quantum computers, albeit on a small scale, are becoming increasingly accessible to the public, for example, through companies like IBM, Google, and D-Wave. Naturally, this calls for leveraging quantum computers to enhance classical Artificial Intelligence (AI), for example, to improve their prediction performance or enable faster training by exploiting quantum mechanical principles such as superposition and entanglement. To this end, there is a growing interest in quantum artificial intelligence (QAI) to exploit quantum computing (QC) to enhance classical AI techniques. This workshop focuses on seeking contributions that encompass both theoretical and applied advances in QAI.

On the other hand, there is also an increasing interest in applying classical AI techniques to solve problems within QC (AI4QC), such as in quantum software engineering, quantum circuit design, and optimizing quantum optimization approaches (e.g., minor embedding in quantum annealing). Consequently, we also seek contributions that apply classical AI techniques in various aspects of QC.

Many AI problems can be cast as optimization problems, and we also welcome contributions formulating AI problems as optimization tasks, e.g., Quadratic Unconstrained Binary Optimization (QUBO) to be solved by quantum annealers.

Topics of Interest

Theoretical foundations of quantum AI algorithms
Quantum AI applications in any domain, e.g., transportation, chemistry, simulations, physics, etc.
Classical AI techniques in the area of quantum circuit design, such as optimizing quantum circuit compilation and transpilation
Quantum noise reduction with classical AI techniques
Classical AI techniques for quantum software engineering, including quantum software testing, debugging, and repair
Applications of large-language models for quantum circuit design and quantum software engineering
Quantum AI techniques for quantum software engineering
Classical AI techniques for optimizing quantum search and optimization algorithms, such as QAOA
Quantum annealing and its applications
QUBO models of AI problems
Novel quantum machine learning algorithms, such as theory and applications of quantum reservoir computing and quantum extreme learning machines

Workshop Format

It will be a one-day workshop. We plan to have an invited keynote speaker, paper presentations, and a panel discussion. We may ask additional speakers if the number of paper submissions is low.

Attendance

There is no special criterion.

Submission Guidelines

The proceedings will be published in the Communications in Computer and Information Science (CCIS) Springer series. Please follow the CCIS formatting instructions. Find more details at https://sites.google.com/view/qcai2026/proceedings

Full Paper: 9-16 Pages
Work in Progress (short papers): 8 pages maximum
Lightning Talks: Only abstract (not in the proceedings)

Submission Site Information: https://easychair.org/conferences?conf=qcai2026

Organizing Committee

Workshop Chair: Shaukat Ali, shaukat@simula.no
Workshop Committee:

Shaukat Ali, Simula Research Laboratory, Norway, shaukat@simula.no
Francisco Chicano, University of Malaga, Spain, chicano@uma.es
Alberto Moraglio, University of Exeter, UK, A.Moraglio@exeter.ac.uk
Francisco Chicano, University of Malaga, Spain, chicano@uma.es
Alberto Moraglio, University of Exeter, UK, A.Moraglio@exeter.ac.uk

Workshop Website:

https://sites.google.com/view/qcai2026

W47: RAI-2026: Workshop on Reproducible AI

The AAAI 2026 Workshop on Reproducible AI (RAI) is a full-day workshop that will take place during the workshop program at AAAI 2026 at the Singapore EXPO on either January 26th or 27th. The workshop aims to bring together researchers and practitioners in the field of artificial intelligence from around the world to present their ongoing work on reproducibility and artificial intelligence, and discuss future directions. We encourage the AI community to investigate the reproducibility, replicability, generalizability and robustness of published results in the AI literature.

Topics of Interest

A reproducability challenge will be issues inviting attempted reproductions or selected work to help the community better understand the value and limitations of LLMs in reproducability, with further details on the webpage. The Reproducibility challenge also welcomes authors to document experiences in attempting to reproduce papers presented at previous AAAI conferences (Main Track) and papers that have been published in the Journal of Artificial Intelligence Research (JAIR).

We welcome submissions of both full technical papers and position papers that conduct novel and unpublished research on reproducibility in AI. Papers should be written in English and formatted according to the AAAI style. Full papers should not exceed eight pages, while position papers must not exceed five pages (unlimited references in both cases). Position papers will be presented alongside regular papers by the authors. Selected papers will be invited for an oral or poster

Workshop Format

1-day workshop with two keynote speakers, oral presentations, and a poster session.

Attendance

Any registered Workshop participant may attend given venue space; posters and talks will be solicted from the Call For Papers and key-note speakers will be invited.

Submission Guidelines

how many pages; list of relevant research work/publications, paper or abstract

Submission Site Information: To be found at https://reproducibleai.github.io/.

Organizing Committee

Workshop Chair:
Odd Erik Gundersen odderik.gundersen@aneo.com
Edward Raff edward.raff@crowdstrike.com
Sagar Samtani ssamtani@iu.edu

Workshop Committee:
The Program Committee is still to be determined.

Workshop Website:

https://reproducibleai.github.io

W48: SECURE-AI4H: Safe, Ethical, Certified, Uncertainty-aware, Robust, and Explainable AI for Health

Artificial Intelligence (AI) is increasingly being deployed in health and biomedical contexts ranging from diagnostics, discovery, and decision support to robotic surgery and hospital operations. These applications demand exceptionally high standards of trustworthiness, safety, security, and transparency. The SECURE-AI4H workshop will explore how to design, certify, and deploy AI systems for healthcare that can help and/or are:

Secure against adversarial threats and data breaches
Ethical and fair across demographic groups
Certified and aligned with regulatory frameworks (e.g., FDA, EMA, Health Canada)
Uncertainty-aware and capable of expressing confidence
Robust to distribution shifts and data quality variations
Drug discovery and drug repurposing
Optimizing workflows in the medical and pharma industry and labs
Explainable to end users, including clinicians and patients
Diagnostics on the edge
Development of novel approaches for diagnostics and sensing

This interdisciplinary workshop invites experts from AI, medicine, ethics, cybersecurity, and regulatory science to discuss technical innovations, emerging standards, and deployment challenges for building real-world, certifiable AI systems in health.

Topics of Interest

Adversarial Robustness in Medical AI
Uncertainty Quantification in Clinical Decisions
Explainable AI for Clinical End-Users
Fairness and Bias Mitigation Across Demographics
Privacy-Preserving Healthcare AI
Regulatory Pathways and Certification Frameworks
Clinical Validation and Real-World Evidence
Robust Medical Imaging Under Distribution Shifts
Safety-Critical Decision Support Systems
Secure Multi-Modal Health Data Integration
Human-AI Collaboration in Clinical Settings
Ethical AI for Vulnerable Populations
Generative AI in Healthcare with Safety Constraints
Formal Verification and Theoretical Guarantees
End-to-End Deployment and MLOps for Healthcare

Workshop Format

Application Area, Invited Speakers, Panels and Presentations

Attendance

Invited speakers from Academia and Industry, paper submissions that are accepted after peer review will be presenting their work as oral/poster

Submission Guidelines

4 page short paper or 8 page full paper in AAAI format.

Submission Site Information: secureai4h@gmail.com

Important Dates

Abstract Registration: October 15^th, 2025
Paper Submission: October 22^nd 2025
Paper Notification: November 5^th, 2025
Camera Ready Submission: November 15^th, 2025

Organizing Committee

Apurva Narayan, apurva.narayan@uwo.ca – Canada – University of Western Ontario
Hong Qin – hqin@odu.edu – United States – Old Dominion University
Yalda Mohsenzadeh – ymohsenz@uwo.ca – Canada – University of Western Ontario
Ritambhara Singh – ritambhara@brown.edu – United States – Brown University
Letu Qingge – lqingge@ncat.edu – United States – North Carolina A&T State University
Elham Dolatabadi – edolatab@yorku.ca – Canada – York University
Laleh Seyyed-Kalantari – lsk@yorku.ca – Canada – York University, Vector Institute
Rishi Ganesan – rishi.ganesan@lhsc.on.ca – Canada – University of Western Ontario
Amol A. Verma – amol.verma@mail.utoronto.ca – Canada – Li Ka Shing Centre for Healthcare Analytics Research and Training
Runjhun Saran Narayan – rsaran@uwaterloo.ca – Canada – University of Waterloo, The University of British Columbia & MOLwise Biosciences Inc.

Workshop Website:

https://idsl-group.github.io/secure-ai4h-aaai2026

Openreview Submission Website: https://openreview.net/group?id=AAAI.org/2026/Workshop/SECURE-AI4H

W49: Shaping Responsible Synthetic Data in the Era of Foundation Models (RDS)

Foundation models (LLMs and multimodal FMs) are increasingly supplemented with synthetic and LLM-generated data – to extend training corpora, fill coverage gaps, and navigate privacy and fairness requirements. At the same time, these models serve as powerful generators of synthetic data for downstream applications, such as training specialized ML models, augmenting datasets in privacy-sensitive domains, and generating test cases for system validation.
But synthetic datasets, whether used to train FMs or generated by them for other uses, introduce a plethora of risks: legal (copyright, consent), security & privacy related (leakage, membership inference), ethical (bias amplification), and technical (model collapse, quality degradation). This workshop examines how synthetic data can be responsibly generated and used to fuel, test, and govern foundation models across their lifecycle (pre-training, fine-tuning, evaluation, auditing), as well as how FM-generated synthetic data impacts downstream applications and systems, and what technical, ethical, and regulatory guardrails are needed across this synthetic data ecosystem.

Topics of Interest

Topics include but are not limited to:

Lifecycle uses of synthetic data, including LLM-driven generation and evaluation
Privacy, security, and data governance (e.g., PETs, leakage risks, provenance)
Fairness, bias, and representation in real/synthetic data mixes
Safety, robustness, and red-teaming with synthetic data
Explainability, interpretability, and uncertainty estimation
Standards, metrics, and tooling for trustworthy synthetic data use
Critical perspectives from law, policy, ethics, and social sciences

Workshop Format

The workshop will run for one day with invited talks, a poster session, spotlight
presentations, and a panel on synthetic data in practice.

Attendance

It is open to all interested attendees, subject to room capacity, with no additional criteria for participation.

Submission Guidelines

We invite submissions of extended abstracts up to 4-pages, with additional pages allowed
for references and appendices

Organizing Committee

Anshu Singh, Singapore’s Government Technology Agency (GovTech), anshu.singh@gt.tech.gov.sg
Shlomi Hod, German Federal Institute for Employment Research (IAB), shlomi.hod@iab.de
Zilong Zhao, Betterdata, zilong@betterdata.ai
Rachel Cummings, Columbia University, rac2239@columbia.edu

Workshop Website:

https://responsible-synthetic-data.github.io/

W50: Graphs and more Complex Structures For Learning and Reasoning (GCLR)

Aim and scope: The study of complex graphs is a highly interdisciplinary field that aims to study complex systems by using mathematical models, physical laws, inference and learning algorithms, etc. Complex systems are often characterized by several components that interact in multiple ways with each other. Such systems are better modelled by complex graph structures such as edge and vertex labelled graphs (e.g., knowledge graphs), attributed graphs, multilayer graphs, hypergraphs, etc. In this workshop, we will focus on various complex structures along with inference and learning algorithms for these structures. The current research in this area is focused on extending existing ML algorithms as well as network science measures to these complex structures. This workshop aims to bring researchers from these diverse but related fields together and embark on interesting discussions on new challenging applications that require complex system modelling and discovering ingenious reasoning methods. We have invited several distinguished speakers with their research interests spanning from the theoretical to experimental aspects of complex networks.

Topics of Interest

The topics of interest include, but are not limited to, the following:

Graph Foundation Models
Fairness-aware Learning in Multilayer Graphs
Benchmarking Foundation Models with Complex Data
Privacy Preservation in Complex Graphs
Causal Inference and Complex Networks
Knowledge Graph-enhanced Foundation Models
Theoretical analysis of graph algorithms or models
Network representation learning and manifold embedding methods
Optimization methods for graphs/manifolds
Link analysis/prediction, node classification, clustering for complex graph structures
Probabilistic and graphical models for structured data
Knowledge graph construction
Social network analysis and measures
Constraint satisfaction and programming (CP), (inductive) logic programming (LP and ILP)

Workshop Format

Submission portal: https://cmt3.research.microsoft.com/GCLR2026/

Important Dates

Poster/Short/Position Paper Submission Deadline: Friday, October 22, 2025 (AoE)
Full Paper Submission Deadline: Friday, October 22, 2025 (AoE)
Paper Notification: Wednesday, November 5, 2025

Attendance

Attendance is open to all. For registration, please refer to the registration guidelines by AAAI-26. Accepted papers will be presented in poster format and some will be selected for oral presentation. At least one author of each accepted submission must be present at the workshop. All presentations must be in person. Virtual Presentations are not permitted. Only virtual attendance will be permitted.

Submission Guidelines

We invite submissions to the AAAI workshop on Learning and Reasoning with Complex Graphs to be held virtually. We welcome the submissions in the following two formats:

Poster/Short/Position paper: We encourage participants to submit preliminary but interesting ideas that have not been published before. These submissions would benefit from additional exposure and discussion that can shape a better future publication. We also invite papers that have been published at other venues to spark discussions and foster new collaborations. Submissions may consist of up to 4 pages plus one additional page solely for references.
Full papers: Submissions must represent original material that has not appeared elsewhere for publication and that is not under review for another refereed publication. Submissions may consist of up to 7 pages of technical content plus up to two additional pages solely for references.

The submissions should adhere to the AAAI paper guidelines. Submission portal: https://cmt3.research.microsoft.com/GCLR2026/

Accepted submissions will have the option of being published on the workshop website. For authors who do not wish their papers to be posted online, please mention this in the workshop submission. The submissions need NOT be anonymized.

Organizing Committee

Organizers:

Deepak Maurya (Purdue University)
Tarun Kumar (Hewlett Packard Labs)
Gayathri Saranathan (Hewlett Packard Labs)

Advisors

Balaraman Ravindran (IIT Madras): Chair
Ginestra Bianconi (Queen Mary University of London)

Workshop Website

https://sites.google.com/view/gclr2026

W51: How Can We Trust and Control Agentic AI? Toward Alignment, Robustness, and Verifiability in Autonomous LLM Agents

Agentic AI marks a new frontier for artificial intelligence: systems that move beyond static prediction to autonomous reasoning, tool use, and sustained collaboration with

humans and society. These agents hold the potential to transform healthcare, education, robotics, and enterprise automation. Realizing this promise requires not only technical advances, but also ensuring that such systems remain aligned with human values, resilient under real-world complexity, and verifiable in ways that inspire lasting trust and effective control.

The AAAI 2026 Workshop on Trust and Control in Agentic AI convenes leading voices from research, industry, and policy to shape this agenda. We invite contributions that advance the principles and practice of alignment, robustness, and verifiability in agentic systems, spanning core algorithms, evaluation methods, institutional frameworks, and governance. By fostering cross-disciplinary dialogue and catalyzing new collaborations, the workshop aims to chart pathways for deploying agentic AI responsibly and at scale, ensuring that its benefits are realized broadly and equitably.

Topics of Interest

We invite submissions on advancing trust and control in agentic AI, with a focus on alignment, robustness, and verifiability.

Trustworthy planning and control in agentic systems
Verification and auditable behavior of agentic LLMs
Personalized agents and consistent persona modeling
Safety-critical embodied agentic AI
Human-centric alignment and feedback integration
Human oversight and control in agentic workflows
Evaluating and benchmarking trust in agentic LLMs
Governance, transparency, and accountability frameworks

Workshop Format

This will be a one-day workshop structured to promote both technical depth and community exchange. The program will feature invited talks by leading researchers, presentations of accepted papers, and interactive discussions around key themes of alignment, robustness, and verifiability in agentic AI. The workshop will conclude with a panel session that brings together perspectives from academia, industry, and government to chart future directions and open challenges for trustworthy agentic AI.

Attendance

The workshop is open to all researchers and practitioners with an interest in trustworthy agentic AI, and no restrictions are placed on participation.

Submission Guidelines

Submissions must be prepared using the AAAI 2026 template. We accept research papers (novel algorithms, theory, or experiments), position papers (provocative perspectives on agentic AI), and survey papers.

Long papers: up to 7 pages of technical content, plus 1 page for references
Short papers: up to 4 pages of technical content, plus 1 page for references

Authors may include supplementary material, but reviewers are not required to review it.

Submission Site Information

Link for the submission can be found on the workshop website.

Important Dates

More information to come

Organizing Committee

Workshop Chair

Haiyan Yin (CFAR, A*STAR, Singapore) – yin_haiyan@a-star.edu.sg

Workshop Committee

Joey Tianyi Zhou (CFAR, A*STAR, Singapore) – joey_zhou@a-star.edu.sg
Sebastian Tschiatschek (University of Vienna) – sebastian.tschiatschek@univie.ac.at
Piotr Koniusz (Data61, CSIRO) – peter.koniusz@data61.csiro.au
Simon See (NVIDIA AI Technology Center) – ssee@nvidia.com

Advisory & Extended Committee: Members from academic and industry partners will be listed on the workshop’s website.

Workshop Website:

https://trustagenticai.github.io/AAAI2026

W52: XAI4Science: From Understanding Model Behavior to Discovering New Scientific Knowledge

Machine learning (ML) models are impressive when they work but they can also show unreliable, untrustworthy, and harmful dangerous behavior. Such behavior is even more common in the era of large models, such as chatGPT, which are quickly being adopted even though we do not understand why they work so well and fail miserably at times. Unfortunately, such rapid dissemination encourages irresponsible use, for example, to spread misinformation or create deep fakes, while hindering the efforts to use them to solve pressing societal problems and advance human knowledge. Ideally, we want models that have a human-like capacity to learn by observing, theorizing, and validating the theories to improve the understanding of the world. At the very least, we want them to aid human knowledge and help us to further enrich it.

Our goal in this workshop is to bring together researchers working on understanding model behavior and show how this key aspect can lead to discovering new human knowledge. This workshop sits at the intersection of eXplainable AI (XAI) and AI4Science. While “BLUE XAI” methods focus on communicating model output to end users, scientific discovery demands “RED XAI” approaches that probe, decompose, and allow re-engineering models themselves. Bridging these two cultures raises questions that neither traditional XAI nor broad AI4Science venues currently cover.

Topics of Interest

The workshop will include theoretical topics on understanding model behavior, namely interpretability and explainability, but also scientific application areas such as weather and climate, healthcare, and material science (AI4Science). These topics are brought together to highlight how seemingly diverse applied scientific fields can leverage XAI for knowledge discovery. The list of topics on theory (Tx) and practice (Px) is given below

(T1) A-priori (i.e., ante-hoc) interpretability and self-explainable models

(T2) A-posteriori (i.e., post-hoc) interpretability and attribution methods

(P1) XAI for knowledge discovery in weather and climate applications

(P2) XAI for knowledge discovery in the healthcare sector

(P3) XAI for knowledge discovery in material science

Workshop Format

The workshop is a one-day meeting, and includes 4 invited talks, 1 panel discussion, 2 contributed talks, 2 flash talk sessions, and 2 poster sessions (1.5 hours each).

Attendance

The organizers do not impose any criteria to attend, other than what AAAI registration imposes. We openly invite anyone with an interest towards XAI for knowledge discovery and related topics to attend the workshop. The maximum number of attendees is to be determined by the room size.

Submission Guidelines

We welcome (optionally anonymous) submissions of ongoing and unpublished work on any topics related to the workshop, including but not limited to the listed topics (T1, T2) and (P1, P2, P3). Each paper will receive at least two reviews. Submissions must be in a single PDF file and are required to use the AAAI 2026 LaTeX template.

We have two tracks:

(1) Regular paper: 6-8 pages, excluding references and appendices.

(2) Short paper: 3-5 pages, excluding references and appendices.

Submission Site

For the sake of a smooth peer review process, all submissions must be made via OpenReview: https://openreview.net/group?id=AAAI.org/2026/Workshop/XAI4Science#tab-your-consoles.

Organizing Committee

Workshop Organizing Committee:

Jiawen Wei (National University of Singapore) – jiawenw@u.nus.edu
Christopher J. Anders (RIKEN AIP) – christopher.anders@riken.jp
Krzysztof Kacprzyk (University of Cambridge) – kk751@cam.ac.uk
Abeba Birhane (Trinity College Dublin) – adbirhane@gmail.com
Sara Hooker (Cohere For AI) – sarahooker@cohere.com
Sebastian Lapuschkin (Fraunhofer Heinrich Hertz Institute) – sebastian.lapuschkin@gmail.com
Gianmarco Mengaldo (National University of Singapore) – mpegim@nus.edu.sg
Workshop Advisory Committee:
Wojciech Samek (Fraunhofer Heinrich Hertz Institute) – wojciech.samek@hhi.fraunhofer.de
Prasanna Balaprakash (Oak Ridge National Laboratory) – pbalapra@ornl.gov
Mihaela van der Schaar (University of Cambridge) – mihaelaucla@gmail.com
Hugues Turbé (University of Geneva) – hugues.turbe@unige.ch

Workshop Website:

https://xai4science.github.io

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