[Event at CIG] Call for Papers – 4th International Workshop on Causality, Agents and Large Models, CALM-27

[Syrine Haddad]

Dear colleagues,

We are pleased to share the Call for Papers for the *4th International
Workshop on Causality, Agents and Large Models (CALM-27)*, which will be
held in conjunction with the *The 18th International Conference on Ambient
Systems, Networks and Technologies (ANT*) on *April 13-15, 2027, Delft, the
Netherlands : *https://www.ciad-lab.fr/calm-27/

*Description:*
Causality, Agents and Large Models (CALM) represents three rapidly-growing
fields within artificial intelligence research.
This workshop aims to establish methodological integration between these
disciplines by investigating how causal reasoning, agentic AI, and
explainable AI (XAI) techniques can enhance transparency, decision-making,
and adaptation in multi-agent systems (MAS).
Agentic AI, which emphasizes autonomous goal-driven behavior and
self-reflective reasoning in AI systems, introduces new paradigms for
control, coordination, and accountability in intelligent agents.
The workshop will provide a forum for researchers to discuss theoretical
foundations, practical applications, and future directions at the
intersection of causal AI, agentic AI, XAI, Large Language Models (LLM),
and MAS.

*CALM Workshop goals:*

   - Explore the role of causal reasoning and agentic AI in enhancing
   decision-making, coordination, and adaptation in multi-agent systems.
   - Discuss technical challenges and opportunities for integrating causal
   AI, agentic AI techniques, and LLM into MAS frameworks.
   - Develop methodologies and metrics to evaluate the explainability and
   autonomy of causal reasoning in agents and large models.
   - Foster interdisciplinary collaboration between researchers in causal
   reasoning, XAI, agentic AI, LLM, and MAS.
   - Examine how explanations support user trust, cognitive ergonomics, and
   effective human-agent collaboration.
   - Identify promising directions for future research and development in
   this rapidly advancing research domain.


*Topics:*
The main topics of the CALM-27 workshop are (but not restricted to):

   - Theoretical foundations of causal reasoning in Multi-Agent Systems
   (MAS)
   - Causal reasoning capabilities in Large Models (e.g., Large Language
   Models, LLMs)
   - Theoretical and practical Agentic-AI for autonomous and reflective
   agents
   - Explainable AI (XAI) with active inference techniques
   - Integration of causal and agentic reasoning for adaptive multi-agent
   coordination
   - Applications of XAI techniques in agent-based modeling and simulation
   - Human-centered evaluation of causal explanations in MAS, LLMs, and
   agentic systems
   - Causal inference in complex and dynamic multi-agent environments
   - Mechanistic Interpretability of Causality in Large Language Models
   - LLMs for coordination, cooperation, and communication among agents
   - Challenges and opportunities for incorporating XAI and agentic
   principles into MAS frameworks
   - Case studies and empirical evaluations of XAI approaches in agents
   - Generative AI as preprocessing for MAS
   - Exploring causality with deep generative models
   - Digital twins and simulators for interpretable synthetic data
   generation
   - Graph neural network causal learning
   - Interpretable and ergonomically-grounded root cause analysis methods
   for agent decision-making
   - Logic and argumentation-based approaches to causal reasoning
   - Ethical and Responsible XAI and Agentic AI in LLM
   - Human Factors in XAI and Agentic AI
   - Adaptive and Personalized Explanations (Context-aware, and
   Human-centric)
   - Multi-modal explanations and Cross-cultural ergonomics
   - Ergonomic evaluation of explanation modalities (visual, textual,
   interactive)
   - Explainability in human-agent teaming: Ergonomic principles for
   effective collaboration in mixed teams (humans + AI agents)


*Submission guidelines:*
Submitted papers must be no longer than 6 pages including all figures,
tables and references.
The submitted paper must be formatted according to the Guidelines of
Procedia Computer Science, Elsevier.
Authors are required to write **The 4th International Workshop on
Causality, Agents and Large Models (CALM-27)** in the title section of the
template, and not the name of the main conference.
Kindly refer to the Template in the CALM-27 website.
Submissions will be reviewed by 2-3 members of the program committee, who
are experts in the field.
The acceptance of the submitted papers will be based on scientific rigor,
methodological soundness, contribution to causality and explainability in
agents and large models, and originality.
Authors are requested to submit their papers electronically as PDF files
via the EasyChair submission page:
https://easychair.org/my2/conference?conf=calm27

*Proceedings :*
All accepted papers will be scheduled for oral presentations.
At least one author of each accepted paper is required to register and
attend the conference to present the work.
All accepted and registered papers will be published by Elsevier in the
open-access Procedia Computer Science series on-line.
Procedia Computer Science is hosted by Elsevier and on Elsevier content
platform ScienceDirect, and will be freely available worldwide.
All papers in Procedia will be indexed by Scopus and by Thomson Reuters’
Conference Proceeding Citation Index, Scopus and Engineering Village (This
includes EI Compendex),and DBLP.
The papers will contain linked references, XML versions and citable DOI
numbers.

*Important Dates : *
Submission deadline: November 27th, 2026
Notification: January 10th, 2027
Final date for camera-ready: January 20th, 2027
Workshop: April 13-15, 2027

All deadlines are at the end of the day specified, anywhere on Earth
(UTC-12).

*Previous proceedings:*
CALM-24 (Springer)
https://link.springer.com/book/10.1007/978-3-031-89103-8
CALM-25 (Springer)
https://link.springer.com/book/10.1007/978-3-032-20548-3
CALM-26 (Elsevier)
https://www.sciencedirect.com/journal/procedia-computer-science/vol/280/suppl/C

Best regards,
*Syrine HADDAD ** <https://orcid.org/0000-0001-8606-0407>*

*Postdoctoral Researcher*
*Ph.D in Computer Science** -*
* Human-Computer Interaction and Deep Learning *
*The University of Technology of Belfort-Montbéliard (UTBM)** | **CIAD
Laboratory (**Connaissances et Intelligence Artificielle Distribuées)*
*Email : *syrine.haddad at utbm.fr | syrineh at sigchi.org
*Personal Link : www.syrinehaddad.com <http://www.syrinehaddad.com>*
*LinkedIn : *syrinehaddad <http://www.linkedin.com/in/syrinehaddad> |*
Google Scholar : *_kIqiIQAAAAJ&hl
<https://scholar.google.com/citations?user=_kIqiIQAAAAJ&hl=en>
*GitHub : **serenahaddad <https://github.com/serenahaddad>*