[Apologies for cross-posting]
ICLR 2026 Workshop - From Human Cognition to AI Reasoning: Models, Methods, and Applications
Rio de Janeiro, Brazil | April 26 or 27, 2026 (Exact date TBD)
Workshop Website
Submission Deadline: February 01, 2026 (AoE, UTC-12)
The objective of this workshop is to bridge the gap between human cognitive science and artificial intelligence by bringing together researchers working on computational models of human cognition, neurosymbolic AI, human-AI interaction, and cognitively-inspired machine learning. Recent advances in AI have demonstrated remarkable capabilities, yet these systems often lack the interpretability, causal reasoning, and generalization abilities that characterize human intelligence. Meanwhile, cognitive science has made significant progress in understanding human reasoning, learning, and decision-making processes. We believe that incorporating insights from human cognition into AI systems can lead to more robust, interpretable, and human-aligned artificial intelligence. This workshop aims to facilitate cross-pollination of ideas between cognitive scientists, neuroscientists, and AI researchers to develop the next generation of AI systems that can reason more like humans while maintaining computational efficiency.
The workshop will focus on research related to all aspects of human cognition and AI reasoning. This topic features technical problems that are of interest across multiple fields, including cognitive science, machine learning, AI planning, human-robot interaction, and neurosymbolic AI. We welcome submissions that address formal as well as empirical issues on topics such as:
Explicit modeling of human knowledge and cognitive capabilities.
Introducing explicit human models into the reasoning process.
How AI can model and reason about human mental states and intentions.
Combining neural and symbolic methods inspired by human cognition.
Incorporating human causal reasoning patterns into AI systems.
Using human cognitive models to make AI systems more interpretable.
Incorporating human teaching and correction into learning processes.
Structured approaches to human-inspired AI reasoning.
Probabilistic approaches to human-like reasoning.
INVITED SPEAKERS (TENTATIVE)
Rachid Alami, LAAS-CNRS, France
Kimberly Lauren Stachenfeld, Google DeepMind and Columbia University, USA
Joshua B Tenenbaum, Massachusetts Institute of Technology, USA
Elmira Yadollahi, Lancaster University, UK
WORKSHOP FORMAT
The workshop will feature invited talks, a selected set of contributed talks, and discussions. The workshop will be in-person and is scheduled for one day. ICLR 2026 will be an in-person event this year, and the workshop will follow the same format as the conference.
SUBMISSION INSTRUCTIONS
Submissions can describe either work in progress or mature work that would be of interest to researchers working on one or more of the topics mentioned above. We also welcome “highlights” papers summarizing and highlighting results from multiple recent papers by the authors, and "blue sky" papers that propose new ideas and directions for future research. Please note that the submitted work must not have previously appeared at any machine learning venue, including the main ICLR conference track.
Submissions of papers being reviewed at other venues (IJCAI, ICML, ICAPS, ACL, UAI, etc.) are welcome since HCAIR is a non-archival venue and we will not require a transfer of copyright. If such papers are currently under blind review, please anonymize the submission.
Two types of papers can be submitted:
IMPORTANT DATES
Paper submission deadline: February 01, 2026 (AoE, 11:59 PM UTC-12)
Author notification: March 01, 2026
Camera-ready deadline: March 10, 2026
Workshop date: April 26 or 27, 2026 (Exact date TBD)
ORGANIZING COMMITTEE
Julie A. Shah, Massachusetts Institute of Technology, USA
Sarath Sreedharan, Colorado State University, USA
Silvia Tulli, Sorbonne University, France