IEEE CogMI 2026 Call for Papers

Important dates:

Round 1 Submission Dates:
Time ZoneAnywhere on Earth
Submission Deadline: June 1, 2026
Notification of Acceptance Date: July 25, 2026
Camera Ready Due: August 15, 2026

Round 2 Submission Dates:
Time ZoneAnywhere on Earth
Submission Deadline: August 15, 2026
Notification of Acceptance Date: September 20, 2026
Camera Ready Due: September 30, 2026

Artificial Intelligence (AI) is rapidly transforming our business, social and physical world. Machine Intelligence and computing artifacts infused with AI and cognition are increasingly becoming an integral part of our social and economic activities/processes, as well as cultural and business interactions. The cognitive machine intelligence (CogMI) is creating unprecedented opportunities for impactful scientific innovations and societal transformations by significantly augmenting the human ability for problem solving and rapid decision making in terms of speed, precision and variety, while fostering innovations in all our human endeavors from scientific discoveries, solving long standing healthcare problems, ' modeling highly interconnected and globally distributed systems, and so forth. As we enter the new social, physical and cyber world empowered by such cognitive machine intelligence, it is important to understand and explore the foundational characteristics of the algorithmic theory and processes behind the cognitive machine intelligence, and now intelligent machines can promote a productive and creative partnership and collaboration between the humans and the machines, so as to ensure an equitable, fair and transparent CogMI-enhanced society and cyber-physical-social worlds around us.

The goal of the IEEE Conference on Cognitive Machine Intelligence (IEEE CogMI) is to create a research and development forum on cognitive machine intelligence to foster research results on AI and ML with cognitive science and behavioral science. It promotes productive collaborations among experts from multiple disciplines, such as but not limited to: computer science, electronic and computer engineering, information science, economics, industrial engineering, psychology, social and behavioral sciences, biological and physical sciences, and ethics, law and policy. IEEE CogMI aims to be a premier, highly multidisciplinary forum to foster interaction and exchange of innovative ideas, and collaboration among researchers, practitioners, philosophers from academia, industry and government sectors who are interested in developing innovative AI/ML algorithms, applications and systems by integrating cognitive science and understanding how AI can support humans instead of replacing them, resulting in productive and desirable social-technical systems

• List of Topics
Topics of interest include, but are not limited to:

• Cognitive Foundations and Architectures
- Cognitive architectures, computational models of mind, and dual-process (System 1/System 2) theories in AI, including agent architectures, models of executive function, adaptive behavior, bounded rationality, and developmental learning trajectories
- Neuroscience-inspired AI (NeuroAI) and neuromorphic computing, including brain-inspired architectures, biologically plausible learning rules, neural coding principles, spiking neural networks, predictive coding, and free-energy frameworks
- Memory, attention, and information processing in artificial systems, including memory-augmented networks, episodic replay, models of forgetting and consolidation, selective attention, cognitive load, information bottlenecks, chunking, and content-addressable memory

• Reasoning and Knowledge
- Commonsense, causal, and abstract reasoning, including analogical and relational reasoning, compositional generalization, spatial and temporal reasoning, counterfactual thinking, and reasoning under uncertainty and incomplete information
- Neuro-symbolic AI and hybrid architectures, including integrating neural learning with symbolic knowledge representation, differentiable logic programming, neural theorem proving, and bridging perception, language, and formal reasoning
- Knowledge representation, ontology, and conceptual structure, including concept formation and categorization, belief revision, epistemic reasoning, knowledge graphs, commonsense knowledge bases, and schema-based understanding of events
- Probabilistic and Bayesian models of cognition, including probabilistic computing, Bayesian learning, rational analysis, reasoning under uncertainty, and probabilistic programming as cognitive modeling frameworks

• Language, Communication, and Meaning
- Large language and vision-language models as cognitive and linguistic objects of study, including what LLMs and VLLMs reveal and fail to reveal about human cognition, language, vision, and meaning; multimodal cognition and visual-linguistic binding; scaling laws and intelligence; in-context learning as cognitive flexibility; chain-of-thought as verbal reasoning
- Natural language processing, generation, and speech through a cognitive lens, including computational psycholinguistics, models of language acquisition, semantic grounding and compositionality, pragmatics, discourse, text analytics, and speech recognition/generation
- Dialogue systems, virtual agents, chatbots, and conversational AI, including communication and common ground theory, cooperative dialogue, audience design, perspective-taking, and interactive storytelling

• Perception, Embodiment, and Action
- Perception, grounding, and embodied cognition, including multimodal integration, language grounding to vision and robotics, affordance learning, intuitive physics, object permanence, active perception, and simulation-based understanding
- Computer vision and image processing as cognitive perception, including visual reasoning, scene understanding, visual question answering, saliency, gaze modeling, and cognitive models of visual search and recognition
- Cognitive robotics, autonomous systems, and human-robot interaction, including sensorimotor learning, cognitive motor planning, spatial navigation, embodied interaction, situated cognition in physical agents, and collaborative robot behavior

• Learning and Adaptation
- Machine learning, neural networks, and deep learning as models of cognition, including few-shot and meta-learning as rapid generalization, curriculum and continual learning, transfer and domain adaptation, inductive biases as cognitive constraints, and self-supervised learning as perceptual development
- Reinforcement learning, planning, and goal-directed behavior, including model-based vs. model-free learning, hierarchical goal decomposition, world models, imagination and look-ahead, curiosity-driven exploration, and test-time adaptive reasoning
- Learning with relational and graph-structured data, including graph neural networks for relational and structural cognition, knowledge organization, relational reasoning, and learning over interconnected conceptual structures

• Social Cognition and Behavior
- Theory of mind, social reasoning, and multi-agent cognition, including modeling beliefs, desires, and intentions of others; joint attention; cooperation and coordination; cultural transmission; negotiation; mechanism design; and algorithmic game theory
- Social computing, computational social science, and social psychology of AI, including modeling collective behavior and social dynamics, group decision-making, social norms, social influence, opinion formation, and AI-mediated social interaction
- Affective computing, emotion, and behavioral science, including emotion recognition and modeling, sentiment analysis, empathy in AI, behavioral models of decision-making, heuristics and biases, and the cognitive-behavioral foundations of human-AI systems

• Creativity and Imagination
- Computational creativity, imagination, and generative cognition, including AI for art, music, design, and scientific ideation; divergent thinking; conceptual blending; generative models as accounts of imagination; evaluating novelty and meaning; and ethical issues of creative AI (authorship, appropriation)

• Human-AI Interaction and Trust
- Human-AI collaboration and mixed-initiative systems, including augmented cognition, human-in-the-loop learning, cognitive aspects of shared decision-making, adaptive interfaces, designing for appropriate mental models of AI, and cognitive workload in human-AI teams
- Trust, reliance, and mental models of intelligent machines, including cognitive foundations of trust calibration, over-reliance and under-reliance, transparency as a trust mechanism, and building/maintaining trust in AI across contexts

• Explainability, Safety, and Alignment
- Explainability, interpretability, and transparency, including cognitive models of explanation, XAI methods (feature attribution, concept-based, natural language), probing neural representations, attention visualization, argumentative explanations, and human evaluation of explanations
- AI safety, value alignment, and responsible AI, including bias, fairness, and equity through cognitive and social science lenses; reward modeling and preference specification; adversarial robustness; moral reasoning; accountability; and the cognitive science of AI risk perception
- Privacy, security, and adversarial machine learning, including privacy-preserving AI, adversarial resilience as cognitive reliability, deceptive alignment, and the societal-cognitive impacts of surveillance and data practices

• Evaluation, Philosophy, and Societal Impact
- Cognitive benchmarking and psychometric evaluation of AI, including testing AI on human cognitive tasks, comparing human and machine reasoning/perception/language, behavioral experiments with AI, compositionality and systematicity metrics, and evaluation methodology for cognitive AI
- Consciousness, metacognition, and self-awareness in AI, including introspection, confidence calibration, self-monitoring and self-correction, computational theories of consciousness, self-models, and philosophical questions about machine understanding
- Philosophy of AI, ethics, and the nature of intelligence, including the grounding problem, narrow vs. general intelligence, moral status of cognitive AI, governance and regulation, and the distinction between understanding and pattern matching
- Societal and cognitive impacts of AI, including effects on human attention, memory, skill, and autonomy; AI in education, health, science, business, and public welfare; cognitive ergonomics of AI-generated content; and responsible deployment across application domains

• Cross-Cutting and Emerging
- Emerging frontiers in cognitive AI, including agentic AI and autonomous tool use, foundation models as cognitive models, retrieval-augmented cognition, multi-agent cognitive ecosystems, AI for scientific discovery, developmental AI, world models as internal simulators, synthetic data as cognitive rehearsal, and AI-assisted cognitive science


• Submission Guidelines

- Research Track Paper Submission
We invite original research papers that have not been previously published and are not currently under review for publication elsewhere. Papers submitted to Research track should be up to 10 pages, anonymously in the standard two column IEEE proceedings format, excluding the bibliography, well-marked appendices, and supplementary material. which can be found at IEEE Manuscript Templates for Conference Proceedings . Authors should not change the font or the margins of the IEEE format. Papers should avoid revealing authors’ identity in the text. When referring to their previous work, authors are required to cite their papers in the third person, without identifying themselves. The papers should be submitted at the research track of the conference in EasyChair.

- Vision Track Paper Submission (By Invitation Only)
We invite original research vision papers that have not been previously published and are not currently under review for publication elsewhere. Vision contributions should focus on blue-sky ideas and research vision in the area that at least one of the lead senior authors are known for. Vision paper can be as short as 2 pages but should be no longer than 10 pages in the standard two column IEEE proceedings format, which can be found at IEEE Manuscript Templates for Conference Proceedings . The papers should be submitted at the vision track of the conference in EasyChair.

- Industry/Government Paper Submission
We invite original industry papers that have not been previously published and are not currently under review for publication elsewhere. At least one co-author must be affiliated with industry or Government organizations, such as labs in DoE, DoD, NIH, NSA Labs. Papers submitted to Regular or Industry/Gov track should be no longer than 10 pages in the standard two column IEEE proceedings format, IEEE Manuscript Templates for Conference Proceedings . The papers should be submitted at the industry/gov track of the conference in EasyChair.

• Workshops Proposals
Proposals for half-day or full day workshops that focus on IEEE CogMI 2024 related themes are solicited. Workshop proposals should be at most 6 pages, including a biographical sketch of each instructor, and submitted to EasyChair.

• Panels Proposals
Proposals for panel discussions that focus on future visions relevant to Cognitive Machine Intelligence are preferred. Potential panel organizers should submit a panel proposal of at most five pages, including biographical sketches of the proposed panelists to the Panel Chairs.

• Tutorials Proposals
Proposals for full and half-day tutorials are solicited. Tutorials are intended to enhance the technical program, and as such they should be relevant to collaborative computing, networking, internet technologies, worksharing, and applications. Potential tutorial presenters should submit a tutorial proposal of at most three pages, including: description of potential audience and background knowledge expected from the audience, if any; tutorial description; biographical sketch(s) of presenter(s).

• Review Policy
IEEE Policy and professional ethics require that referees treat the contents of papers under review as privileged information not to be disclosed to others before publication. It is expected that no one with access to a paper under review will make any inappropriate use of the special knowledge, which that access provides. Contents of abstracts submitted to conference program committees should be regarded as privileged as well, and handled in the same manner. The Conference Publications Chair shall ensure that referees adhere to this practice.

Organizers of IEEE conferences are expected to provide an appropriate forum for the oral presentation and discussion of all accepted papers. An author, in offering a paper for presentation at an IEEE conference, or accepting an invitation to present a paper, is expected to be present at the meeting to deliver the paper. In the event that circumstances unknown at the time of submission of a paper preclude its presentation by an author, the program chair should be informed on time, and appropriate substitute arrangements should be made. In some cases it may help reduce no-shows for the Conference to require advance registration together with the submission of the final manuscript.

• Awards
IEEE CogMI will feature a Best Paper award and a Best Student Paper award (to be selected by the program committee/best paper award team). A paper is eligible for the Best Student Paper award if the first author is a full-time student at the time of submission. A partial travel grant or cash award may be offered to the winner student depending on fund availability.

• Program Co-Chairs
Jerry Jialie Shen, University of London, UK
Amarda Shehu, George Mason University, USA
Reza Zafarani, Syracuse University, USA