*AIASci 2026: End-to-End Research Workflow, Methods, and Applications for AI-Assisted Science*

ARR Commitment Deadline: October 20, 2026 Submission link: https://openreview.net/group?id=aiasplus.org/AIAS/2026/Workshop/AIASci_ARR_C...

Full-day workshop co-located with AIAS 2026 - November 7, 2026 in San Francisco, USA

*Aim and Scope* Large (vision-) language models are transforming the practice of science — from literature search and hypothesis generation to automated experimentation, manuscript drafting, multimodal content creation, and peer review. The central question is no longer whether AI can assist with these tasks, but how such systems can support the end-to-end research workflow: sustaining long-horizon scientific reasoning, grounding their claims in causal and mechanistic understanding, adapting through interaction with real experimental environments, and serving as trustworthy partners across scientific disciplines.

AIASci brings together researchers building systems that move beyond passive analysis toward sustained reasoning, causal understanding, adaptive learning, and iterative participation in scientific discovery. Mirroring the workshop's title, the program is structured along three intersecting pillars that submissions may combine freely. Scientific responsibility (ethics, reliability, and human oversight), runs as a unifying theme throughout, featured in invited talks, contributed papers, poster sessions, and a dedicated panel discussion.

Website: https://aiasciworkshop.github.io/

*Topics of Interest* We welcome submissions on any aspect of human–AI collaboration for science, the capabilities that enable it, and the evaluation of AI-assisted scientific outputs. Topics include, but are not limited to:

(1) Stages of the AI-Assisted Research Workflow - Literature search, synthesis, comparison, and recommendation - Hypothesis generation, research ideation, and idea refinement - Automated experimentation, AI agents, and AI-driven discovery - Text-based scientific content generation - Multimodal scientific content generation and understanding - AI-assisted peer review, claim verification, and meta-review generation

(2) Methodological Capabilities - Long-context and sustained scientific reasoning - Causal inference and mechanistic understanding - Adaptive systems that learn through experimentation, feedback, and reflection - Verification, reliability, and reproducibility of scientific AI outputs - Human-in-the-loop and human–AI collaboration designs - Multimodal models, self-improvement, and memory for scientific AI systems - Evaluation of AI-assisted research workflows and their outputs

(3) Scientific Domains and Applications - AI scientists for physics, mathematics, chemistry, materials science, biology, medicine, and related disciplines - AI for the social sciences and humanities - Cross-disciplinary case studies - AI-assisted reproductions of real scientific findings

*Paper Submission* - Long papers: up to 8 pages of content, plus unlimited references - Short papers: up to 4 pages of content, plus unlimited references

*Submission Modes* - Direct submissions: Receive up to three reviews and a final acceptance decision. - ARR commitments: Unpublished papers already reviewed through ACL Rolling Review may be committed to the workshop. - Non-archival and previously published papers

*Submission Links* - Direct submissions: https://openreview.net/group?id=aiasplus.org/AIAS/2026/Workshop/AIASci - ARR commitments: https://openreview.net/group?id=aiasplus.org/AIAS/2026/Workshop/AIASci_ARR_C... - Workshop website: https://aiasciworkshop.github.io/

*Important Dates* - Direct submission deadline (extended): September 18, 2026 - ARR commitment deadline: October 9, 2026 - Notification of acceptance: October 16, 2026 - Camera-ready papers due: October 26, 2026 - Previously published papers deadline: October 26, 2026 - AIASci 2026 workshop: November 7, 2026

*Organizing Committee* - Yong Cao, University of Tübingen - Steffen Eger, University of Technology Nuremberg - Anne Lauscher, University of Hamburg - Yufang Hou, IT:U Austria and IBM Research - Wei Zhao, University of Aberdeen

Contact: yong.cao@uni-tuebingen.de