AIAS 2026 invites workshop proposals on emerging topics at the frontier of AI for scientific discovery. Workshops should bring together researchers working on focused themes, open challenges, and new directions that can shape the future of AI as a partner in science.
In particular, we welcome workshop proposals aligned with the vision of ‘Discoverative AI’: AI systems that move beyond passive analysis toward sustained reasoning, causal understanding, adaptive learning, and participation in iterative discovery workflows. The conference’s published vision page frames this direction around structural capabilities such as long-term memory, causal reasoning, world modeling, metacognition, and discovery-oriented intelligence.
Potential workshop themes include, but are not limited to:
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Long-Context and Multi-Step Reasoning: methods for maintaining coherence, correctness, and scientific utility across extended chains of reasoning, large documents, and complex research contexts.
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Causal Inference and Mechanistic Understanding: approaches that move beyond statistical association toward causal, explanatory, and mechanism-level understanding in scientific domains.
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Adaptive and Self-Improving AI Systems: methods that enable AI systems to improve through feedback, iteration, reflection, or interaction with experimental and real-world environments.
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World Models and Hypothesis-Driven AI: internal modeling approaches that support prediction, counterfactual reasoning, planning, and the generation and testing of scientific hypotheses.
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Research Pipeline Automation: AI tools and frameworks for automating literature synthesis, data interpretation, experimental design, simulation workflows, and other components of the research process.
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Verification, Reliability, and Safety for Scientific AI: techniques for evaluating correctness, robustness, reproducibility, oversight, and safe deployment of AI systems used in scientific discovery.
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Memory, Metacognition, and Discovery-Oriented Architectures: architectures that support persistent knowledge, uncertainty awareness, curiosity-driven exploration, and other capabilities relevant to discovery.
We especially encourage workshops that do one or more of the following:
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define a frontier research agenda
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connect AI advances to concrete scientific use cases
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propose benchmarks, datasets, or shared evaluation tasks
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bring together communities across AI and scientific discipline
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examine both technical opportunity and scientific responsibility
Workshops should aim to stimulate deep discussion, surface open problems, and create focused communities around promising directions in AI for accelerated research.
Workshops will complement the main AIAS+ 2026 symposium to be held in-person in the San Francisco from November 5-7 2026. Submission Guidelines.
Please use the AIAS 2026 Workshop Proposal Template for your submission. Submit the workshop proposal through this form. Workshop proposals will be reviewed based on the quality of the proposal, their relation to the proposed topics, and the hosting capacity of the conference.
Requirement for In-Person Activities
The AIAS+ 2026 will be held in-person in San Francisco, USA. To enhance the in-person experience, it is required for each workshop's organizers to be attending the conference and organizing the workshop in-person.
Important Dates
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Workshop Submission Deadline: May 1, 2026
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Notification of Acceptance: May 15, 2026
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Conference Dates: November 5-7, 2026
Exact workshop paper submission and author notification due dates are at the discretion of workshop organizers.
Workshop papers will not be included in the AIAS 2026 proceedings. Any decision on if and where proceedings are to be archived is left to the organizers. To help preserve the authors’ ability to submit a revised version of their paper to a conference or journal, joining the volume is suggested to be left at the discretion of the authors.
AIAS Conflict of Interest Policy
All authors must adhere to the AIAS Conflict of Interest policy.
AIAS Policy Against Harassment
All authors and participants must adhere to the AIAS Policy Against Harassment.