From Human Cognition to AI Reasoning:
Models, Methods, and Applications

Overview

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 explore how explicit models of human knowledge, cognitive capabilities, and mental states can be integrated into AI reasoning processes. We will examine approaches that combine neural and symbolic methods inspired by human cognition, incorporate human causal reasoning patterns, and leverage human teaching signals to create more interpretable and aligned AI systems.

Call for Papers

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.

Submission Guidelines

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:

• full papers with the length of up to 9 pages + references
• short papers with the length between 3 and 5 pages + references

Submissions may use as many pages of appendices (after the references) as they wish, but the reviewers are not required to read the appendix. Submissions should use the ICLR 2026 paper format. The papers should adhere to the ICLR Code of Ethics and ICLR 2026 policy on using LLMs for writing in their paper. Papers can be submitted via OpenReview at https://openreview.net/group?id=ICLR.cc/2026/Workshop/HCAIR.

Important Dates

Invited Talks

Rachid Alami LAAS-CNRS, France	Reasoning on human beliefs and decisions and Integrating their anticipation in a Human-Aware Robot Task Planner We address the task planning problem for cognitive and interactive robots collaborating with humans to achieve a shared task or assisting human in a task. Two main aspects will be discussed: (1) the ability to reason about Theory of Mind and potential divergence of beliefs between the Human and the robot and to plan corrective actions and communications if needed; and (2) the elaboration of a concurrent and compliant joint action model based on social and collaborative signals. This model captures subtle possible agents' coordination and the human's inherent uncontrollability. We illustrate how this model is used to explore relevant courses of action and guide our planning approach. The result is a behavioral policy capturing the best robot actions to perform to be congruent and compliant with any online human's decision and action, including being passive. The policy also aims to best satisfy an estimation of human preferences. Bio: Dr. Rachid Alami is a Senior Scientist Emeritus (Directeur de Recherche Emérite CNRS) at the Laboratory for Analysis and Architecture of Systems (LAAS). He is a member of the Artificial and Natural Intelligence Toulouse Institute (ANITI). and served as its academic chair ) of Cognitive and Interactive Robotics from 2019 to 2024. Dr. Alami received an engineer diploma in computer science from ENSEEIHT in 1978, a Ph.D in Robotics from Institut National Polytechnique in 1983 and an Habilitation HDR from Paul Sabatier University in 1996. He has contributed to and taken on responsibilities in several national, European and international research and/or collaborative projects including ESPRIT: MARTHA, PROMotion, and IST FP6 projects: COGNIRON, URUS, PHRIENDS, and FP7 projects: CHRIS, SAPHARI, ARCAS, SPENCER, and H2020: MuMMER, and Horizon Europe: euROBIN. He has also been Principal Investigator in several French national projects: VAP-RISP for planetary rovers, and several ANR and France 2030 projects. His main research contributions fall within the fields of robot decisional and control architectures, task and motion planning, multi-robot cooperation, and human-robot interaction. url: https://homepages.laas.fr/rachid/
Been Kim Google DeepMind, USA	Better Together: The Case for Human-AGI Synergy TBD Bio: Been Kim is a director at Google DeepMind, dedicated to fostering effective communication and collaboration between humans and complex machine learning models. Her research aims to harness machine intelligence for human benefit. Notably, her recent work in teaching superhuman chess concepts to grandmasters, one of them becoming the youngest World Chess Champion (Gukesh). Dr. Kim is an accomplished speaker, having given a talk at the G20 meeting in Argentina (2019) and keynotes at ICLR (2022) and ECML (2020). Her influential work, TCAV, was recognized with the UNESCO Netexplo award and featured at Google I/O '19. Her contributions are also discussed in Brian Christian's book, "The Alignment Problem." A leader in the ML community, she is the General Chair for ICLR 2024, was Senior Program Chair for ICLR 2023, and is on the advisory board for TRAILS and the steering committee for SATML. She has extensive experience as a Senior Area Chair for conferences such as NeurIPS, ICML, ICLR, and AISTATS. She earned her PhD from MIT.
Cedegao Zhang MIT, USA	Game Reasoning in Humans and Machines Games have long been a productive testbed for research in both natural and artificial intelligence. Classic studies on human chess playing led to influential theories of planning and problem solving. Systems such as Deep Blue, AlphaGo, Pluribus, and Cicero represent landmark achievements in AI. Yet much of this work has focused on expert or superhuman performance. A remarkable feature of human intelligence, by contrast, is that we can make intuitively reasonable judgments and decisions even without much experience. In this talk, I present recent work on modeling how people reason about novel games. We show that people are systematic and adaptively rational in how they play a game for the first time and evaluate a game (e.g., how fair or how fun it is likely to be) before they have played it even once. We explain these capacities via the Intuitive Gamer, a computational model based on mechanisms of fast and flat goal-directed probabilistic simulation. Across large-scale behavioral studies with over 1000 participants and 121 two-player strategic board games, our model quantitatively captures human judgments and decisions. I then show how well a battery of language models reasons about such games compared to human data. Reasoning models are generally more aligned with people in their evaluations of games than non-reasoning language models, but there are still notable differences. More broadly, our work offers insights into how people effectively think about novel problems and could inform the design of aligned AI systems that determine not just how to solve tasks, but whether a task is worth thinking about at all. Bio: Ced Zhang is a 4th-year PhD student at MIT, advised by Josh Tenenbaum and Roger Levy. His resesarch approches AI and cognitive science from interdisciplinary perspectives. He is interested in evaluating and improving the reasoning and problem solving capabilities of language model agents, taking inspirations from how humans learn and think. His work has been published in venues including Nature Human Behavior, ICLR, EMNLP, and CogSci and recognized with an EMNLP Outstanding Paper Award. .

Program

Accepted Papers

• Decomposing ARC Programs to Create Simpler Tasks
  Matthew Simpson and Soumya Banerjee
• Affective Multimodal Agents with Proactive Knowledge Grounding for Aligned Marketing Dialogue
  Ernie Tian, Xiaofei Han, Yifei Kang, and Xuanjing Chen
• Intelligent Robot Manipulation Requires Self-Directed Learning
  Li Chen, Chonghao Sima, Kashyap Chitta, Antonio Loquercio, Ping Luo, Yi Ma, and Hongyang Li
• Understanding the Anchoring Effect of LLM with Synthetic Data: Existence, Mechanism, and Potential Mitigations
  Yiming Huang, Biquan Bie, Zuqiu NA, Weilin Ruan, Songxin Lei, Yutao Yue, and Xinlei He
• A Human-inspired Framework for Continuous Spacial Reasoning with Tangram Puzzles
  Yikun Zong and Cheston Tan
• The Decrypto Benchmark for Multi-Agent Reasoning and Theory of Mind
  Andrei Lupu, Timon Willi, and Jakob Nicolaus Foerster
• Do Large Language Models Mentalize When They Teach?
  Sevan K Harootonian, Mark K Ho, Thomas L. Griffiths, Yael Niv, and Ilia Sucholutsky
• From Examples to Solutions: A Cognitive Framework for LLM Code Generation
  Shashwat Saxena, Shreyas Kowshik, and Vikhyath Kothamasu
• Discovering Differences in Strategic Behavior between Humans and LLMs
  Caroline Wang, Daniel Kasenberg, Kim Stachenfeld, and Pablo Samuel Castro
• Failing to Falsify: Evaluating and Mitigating Confirmation Bias in Language Models
  Ayush Rajesh Jhaveri, Anthony GX-Chen, Ilia Sucholutsky, and Eunsol Choi
• The Era of Real-World Human Interaction: RL from User Conversations
  Chuanyang Jin, Jing Xu, Bo Liu, Leitian Tao, Olga Golovneva, Tianmin Shu, Wenting Zhao, Xian Li, and Jason E Weston
• A Framework for Aligning Human Linguistics and AI Perception
  Joseph Charles Bingham
• Cognitive Models as Evaluation Primitives in Humanoid Factors
  Xinyuan Liu, Eren Sadikoglu, Ransalu Senanayake, and Lixiao Huang
• MIRROR: Converging Cognitive Principles as Computational Mechanisms for AI Reasoning
  Nicole Hsing
• Human-Guided Causal Hypothesis Testing for Remote Sensing Anomaly Detection
  Kaustubha V
• MIND: Multi-Agent Inference for Negotiation Dialogue in Travel Planning
  HUNMIN DO, Taejun Yoon, and Ki Yong Jung
• CoTZero: Annotation-Free Human-Like Vision Reasoning via Hierarchical Synthetic CoT
  Chengyi Du, Yazhe Niu, Dazhong Shen, and Luxin Xu
• Directional Confusions Reveal Inductive Bias Through Rate–Distortion Geometry
  Leyla Roksan Caglar, Pedro A. M. Mediano, and Baihan Lin
• From Interaction to Abstraction: Using Behavior and Brain Activity to Evaluate How AI Systems Learn Games
  Botos Csaba, Sreejan Kumar, Austin Tudor David Andrews, Rui Ponte Costa, Marcelo G Mattar, and Momchil Tomov
• Know the Patient, Not Just the Disease: Modeling Human Mental States Through Graph-Based Relational Reasoning
  Ratna Kandala, Akshata Kishore Moharir, and Niva Manchanda
• Multi-shot AutoInterp: Agents Can Explain Complex Features By Refining Explanations
  Kamal Maher, Simon Elias Schrader, and Kola Ayonrinde
• Representing Expertise Accelerates Learning from Pedagogical Interaction Data
  Dhara Yu, Karthikeya Kaushik, and Bill D Thompson
• Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning
  Christopher Pinier, Sonia Acuna Vargas, Mariia Steeghs-Turchina, Dora Matzke, Claire E Stevenson, and Michael Nunez
• Amodal Completion of Occluded Objects in Deep Neural Networks: A Psychophysical Investigation
  Arnav Shakya and Vaibhav Tripathi
• From Human Intuition to Causal Graphs: A DOLCE-Based Human-Ensembled Neuro-Symbolic Architecture
  Manojshyaam C J
• Toward a Reasoning Curriculum for Brain-Trained Foundation Models
  Maël Donoso
• Math Takes Two: A Test for Emergent Mathematical Reasoning in Communication
  Samuel Cooper and Michael Harry Cooper
• Evidence of a Cognitive Shift in AI Education: How Students Are Rethinking Human Intelligence?
  Islem Rekik
• Alignment has a Fantasia Problem
  Nathanael Jo, Zoe De Simone, Mitchell L Gordon, and Ashia C. Wilson
• Training Emergent Joint Associations: A Reinforcement Learning Approach to Creative Thinking in Language Models
  Mukul Singh, Ananya Singha, Aishni Parab, Mansi Uniyal, Pronita Mehrotra, and Sumit Gulwani
• A Cognitive Lens on Selective Memory in Neural Sequence Models: Surprise, Replay, and Consolidation
  SoHyung Kim and Jea Kwon
• Mechanisms of Introspective Awareness
  Uzay Macar, Li Yang, Atticus Wang, Peter Wallich, Emmanuel Ameisen, and Jack Lindsey
• Do LLMs Share Human-Like Biases? Causal Reasoning Under Prior Knowledge, Irrelevant Context, and Varying Compute Budgets
  Hanna Dettki, Charley M Wu, and Bob Rehder
• Testing Behavioral Theories of Motivation in Atari Agents
  Maria Gabriela Valeriano and Esther Luna Colombini
• Cognitive Foundations for Reasoning and Their Manifestation in LLMs
  Priyanka Kargupta, Shuyue Stella Li, Haocheng Wang, Dean Light, Jinu Lee, Shan Chen, Orevaoghene Ahia, Kerem Oktar, Thomas L. Griffiths, Max Kleiman-Weiner, Jiawei Han, Asli Celikyilmaz, and Yulia Tsvetkov
• Democratic ICAI: Debating Our Way to Steering Principles from Preferences
  Kevin Kingslin, Anish Natekar, Ashutosh Ranjan, Vivek Srivastava, Savita Bhat, and Shirish Karande
• Detection of Adversarial Intent in Human-AI Teams Using LLMs
  Abed K. Musaffar, Ambuj Singh, and Francesco Bullo
• Me, Myself, and π: Evaluating and Explaining LLM Introspection
  Atharv Naphade, Samarth Bhargav, Sean Lim, and McNair Shah
• Learning Transferable Latent User Preferences for Human-Aligned Decision Making
  Alina Hyk and Sandhya Saisubramanian
• A Cortically Inspired Architecture for Modular Perceptual AI
  Prerna Luthra
• MindZero: Learning Online Mental Reasoning With Zero Annotations
  Shunchi Zhang, Jin Lu, Chuanyang Jin, Yichao Zhou, Zhining Zhang, and Tianmin Shu
• Gemma Needs Therapy: Investigating and Mitigating Emotional Instability in LLMs
  Anna Soligo, Vladimir Mikulik, and William Saunders
• Wild Guesses and Mild Guesses in Active Concept Learning
  Anirudh Chari and Neil Pattanaik
• When AI Agents Disagree Like Humans: Reasoning Trace Analysis for Human-AI Collaborative Moderation
  Michał Wawer and Jaroslaw A. Chudziak
• Hidden Meanings in Plain Sight: RebusBench for Evaluating Cognitive Visual Reasoning
  Seyed Amir Kasaei, Arash Marioriyad, Mahbod Khaleti, MohammadAmin Fazli, Mahdieh Soleymani Baghshah, and Mohammad Hossein Rohban
• Who Prefers Structured Reasoning? AI Judges Do, Domain Experts Split
  Jordan Rubin

Organizing Committee

Julie A Shah

Massachusetts Institute of Technology, USA

Sarath Sreedharan

Colorado State University, USA

Silvia Tulli

Sorbonne University, France

Pulkit Verma

Indian Institute of Technology Madras, India

On-Site Organizing Team

Kelsey Sikes

Colorado State University, USA

Dennis Kim

Colorado State University, USA

Program Committee

• Nitay Alon, Hebrew University of Jerusalem, Israel.
• Soumya Banerjee, University of Cambridge, UK.
• Marlene D. Berke, Massachusetts Institute of Technology, USA.
• Joseph Charles Bingham, Technion - Israel Institute of Technology, Israel.
• Leyla Roksan Caglar, Icahn School of Medicine at Mount Sinai, USA.
• Li Chen, The University of Hong Kong, Hong Kong.
• Samuel Cooper, Engitix Therapeutics, UK.
• Botos Csaba, Agitprop AI, UK.
• Fei Dai, University of California, Berkeley, USA.
• Zoe De Simone, Massachusetts Institute of Technology, USA.
• Maël Donoso, Ouroboros Neurotechnologies, France.
• Ziyang Guo, Northwestern University, USA.
• Philip Hopkins, Colorado State University, USA.
• Lixiao Huang, Arizona State University, USA.
• Nathaniel Imel, New York University, USA.
• Yanru Jiang, University of California, Los Angeles, USA.
• Chuanyang Jin, Johns Hopkins University, USA.
• Priyanka Kargupta, University of Illinois at Urbana-Champaign, USA.
• Ratna Kandala, University of Kansas, USA.
• SoHyung Kim, Korea Advanced Institute of Science & Technology (KAIST), South Korea.
• Jiyi Li, Hokkaido University, Japan.
• Sonia Krishna Murthy, Harvard University, USA.
• Abed K. Musaffar, University of California, Santa Barbara, USA.
• Christopher Pinier, University of Amsterdam, Netherlands.
• Islem Rekik, Imperial College London, UK.
• Gözde Gül Şahin, Koç University, Türkiye.
• Sandhya Saisubramanian, Oregon State University, USA.
• Anna Soligo, Imperial College London, UK.
• Xinyuan Song, Emory University, USA.
• Cheston Tan, A*STAR - Agency for Science, Technology and Research, Singapore.
• Vaibhav Tripathi, Indian Institite of Technology Gandhinagar, India.
• Caroline Wang, University of Texas at Austin, USA.
• Li Yang, Meta MRS AI, USA.
• Eunice Yiu, University of California, Berkeley, USA.
• Tan Zhi-Xuan, National University of Singapore, Singapore.