Please, accept our apologies in case of multiple copies of this CFP.

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The 2nd International Workshop on Causality, Agents and Large Models (CALM)

https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.ciad-lab.fr%2Fthe-2nd-international-workshop-on-causality-agents-and-large-models-calm%2F&data=05%7C02%7Cclean-list%40science.ru.nl%7C28cc79fe4a4347c6f80e08de065ff184%7C084578d9400d4a5aa7c7e76ca47af400%7C1%7C0%7C638955206328438874%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=fGo3FKRUD2SYx1UyXBNGj6ASrYQLeOULj3HoaxvwcNM%3D&reserved=0

In conjunction with the Luxembourg Logic & AI Summit
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University of Luxembourg, Luxembourg, 01-05 December 2025.

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Description:
============
Causality, Agents and Large Models (CALM) represents three rapidly-growing 
fields within artificial intelligence research.
This workshop aims to bridge the gap between these disciplines by investigating 
how causal reasoning and explainable AI (XAI) techniques can enhance the 
capabilities of multi-agent systems (MAS).
The workshop will provide a forum for researchers to discuss theoretical 
foundations, practical applications, and future directions at the intersection 
of causal AI, XAI, Large Language Models (LLM) and MAS.

CALM Workshop goals:
=====================
-Explore the role of causal reasoning in enhancing decision-making, 
coordination, and adaptation in multi-agent systems.
-Discuss technical challenges and opportunities for integrating causal AI 
techniques and LLM into MAS frameworks.
-Develop methodologies and metrics to evaluate the explainability of causal 
reasoning in agents and large models.
-Foster interdisciplinary collaboration between researchers in causal 
reasoning, XAI, 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:
=======
-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
-Explainable AI (XAI) with active inference techniques
-Applications of XAI techniques in agent-based modeling and simulation
-Human-centered evaluation of causal explanations in MAS and LLMs
-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 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 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:
======================
Submissions will be reviewed by at least three members of the program 
committee, who are experts in the field. The acceptance of the submitted papers 
will depend on their quality, relevance, and originality.
All accepted papers will be published in the Springer proceedings 
Communications in Computer and Information Science (CCIS) series devoted to the 
publication of proceedings of computer science conferences.

Participants are therefore invited to submit
Long papers: up to 16 pages in length (excl. references) or
Short papers: up to 5 pages in length (excl. references).

Papers must be edited using the LNCS format and have to be submitted 
electronically as PDF files via the EasyChair submission page:
https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Feasychair.org%2Fmy%2Fconference%3Fconf%3Dcalm25&data=05%7C02%7Cclean-list%40science.ru.nl%7C28cc79fe4a4347c6f80e08de065ff184%7C084578d9400d4a5aa7c7e76ca47af400%7C1%7C0%7C638955206328464979%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=K3XBsRxtx7DC9DU%2B9dErhTNGlqE72R49mmkdVssiYC0%3D&reserved=0

Chairs:
=======
-Dr. Yazan Mualla, Associate Professor, CIAD, Belfort-Montbéliard University of 
Technology
yazan.mualla[at]utbm.fr
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-Dr. Liuwen Yu, Postdoc, University of Luxembourg
liuwen.yu[at]uni.lu
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-Dr. Hui Zhao, Assistant Professor, (Tongji University, China).
huizhao[at]tongji.edu.cn
-Dr. Amro Najjar, Assistant Professor, (Luxembourg Institute of Science and 
Technology, Luxembourg).
amro.najjar[at]list.lu
-Dr. Davide Liga, Postdoctoral Researcher, University of Luxembourg
[email protected]

Advisory Board:
===============
-Prof. Dr. Stéphane Galland, Full professor, CIAD, Belfort-Montbéliard 
University of Technology
stephane.galland[at]utbm.fr
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-Prof. Dr. Abdeljalil Abbas-Turki, Full professor, CIAD, Belfort-Montbéliard 
University of Technology
abdeljalil.abbas-turki[at]utbm.fr
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Important Dates
================
Paper submission deadline: October 16th, 2025 (final)
Notification of acceptance: October 31st, 2025
Camera-ready: December 13th, 2025
Workshop dates: December 03rd, 2025

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

Previous proceedings:
=====================
CALM-24 (Springer)
https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-3-031-89103-8&data=05%7C02%7Cclean-list%40science.ru.nl%7C28cc79fe4a4347c6f80e08de065ff184%7C084578d9400d4a5aa7c7e76ca47af400%7C1%7C0%7C638955206328516102%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=tJITwdXT9%2Fzq8rO9ReMFz0tHkvHXc2is4gFhCDQCHuY%3D&reserved=0
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