EMERGE’s findings will be communicated at scientific conferences and published in open-access journals. Find below the current list of publications.
Access all publication on the project's Zenodo community clicking here.
Learning causal abstractions of linear structural causal models
This work tackles the issues of modelling causal knowledge for linear causal models with linear abstraction functions.
Riccardo Massidda, Sara Magliacane and Davide Bacciu, 2025, in Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, 118, 30.
Learning Low-Dimensional Strain Models of Soft Robots by Looking at the Evolution of Their Shape with Application to Model-Based Control
This paper introduces a streamlined method for learning low-dimensional, physicsbased models that are both accurate and easy to interpret.
R. Valadas, M. Stölzle, J. Liu and C. D. Santina, 2025 IEEE 8th International Conference on Soft Robotics (RoboSoft), Lausanne, Switzerland, 2025, pp. 1-8.
Learning Multi-Reference Frame Skills from Demonstration with Task-Parameterized Gaussian Processes
This work proposes to learn such a representation without using task-specific heuristics within the context of multi-reference frame skill learning by superimposing local skills in the global frame.
M. R. Montero, G. Franzese, J. Kober and C. D. Santina, 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Abu Dhabi, United Arab Emirates, 2024, pp. 2832-2839, doi: 10.1109/IROS58592.2024.10803060.
Less is More: An Analysis of Minimal Information Sharing on the Performance of Robot Swarms
This paper investigates the impact of minimal global information sharing on the coordination and performance of robot swarms.
H. Hickson, S. Lee, S. Jones, T. Didiot-Cook, S. Hauert and A. Mavromatis, 2025 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), Tokyo, Japan, 2025, pp. 1-8, doi: 10.1109/ACSOS-C66519.2025.00024.
Leveraging LLMs to Build a Semi-synthetic Dataset for Legal Information Retrieval: A Case Study on the Italian Civil Code and GPT4-O
This work evaluates the applicability of LLMs for the automatic generation of a dataset of legal query-passage pairs to train retrieval systems.
Mattia Proietti, Lucia C. Passaro, and Alessandro Lenci. 2025. In Proceedings of the Eleventh Italian Conference on Computational Linguistics (CLiC-it 2025), p. 933–941.
Lifelong Evolution of Swarms
This paper introduces a lifelong evolutionary framework for swarms, where a population of swarm controllers is evolved in a dynamic environment that incrementally presents novel tasks.
Lorenzo Leuzzi, Davide Bacciu, Sabine Hauert, Simon Jones, and Andrea Cossu. 2025. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '25). DOI: 10.1145/3712256.3726384
LLMs Struggle on Explicit Causality in Italian
This paper presents ExpliCITA, a translation of the English ExpliCa dataset, which is the first publicly available dataset for joint temporal-causal reasoning in Italian, enabling evaluation of LLMs on Italian PCD.
Alessandro Bondielli, Martina Miliani, Luca Paglione, Serena Auriemma, Lucia Passaro and Alessandro Lenci, CLiC-it 2025: Eleventh Italian Conference on Computational Linguistics, September 24 — 26, 2025, Cagliari, Italy
MAIA: a Benchmark for Multimodal AI Assessment
This paper introduces MAIA (Multimodal AI Assessment), a multimodal dataset developed as a core component of a competence-oriented benchmark designed for fine-grained investigation of the reasoning abilities of Visual Language Models (VLMs) on videos.
Davide Testa, Giovanni Bonetta, Raffaella Bernardi, Alessandro Bondielli, Alessandro Lenci, Alessio Miaschi, Lucia Passaro and Bernardo Magnini, CLiC-it 2025: Eleventh Italian Conference on Computational Linguistics, September 24 — 26, 2025, Cagliari, Italy
MAINLE: A Multi-Agent, Interactive, Natural Language Local Explainer of Classification Tasks
This paper introduces a multi-agent architecture to provide interactive explanations for classification tasks based on a range of machine learning algorithms, so that end-users can obtain answers in natural language.
Serafim, P.B. et al. (2026). Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2025. Lecture Notes in Computer Science, vol 16016. DOI: 10.1007/978-3-032-06078-5_9
Mental Models in Human-AI Interaction: Systematic Review of Empirical Methodologies and Guidelines
This work reviews 88 empirical studies that elicit humans’ mental models of AI systems.
Téo Sanchez, Oleksandra Vereschak, and Ophelia Deroy. In Proceedings of the 31st International Conference on Intelligent User Interfaces (IUI '26), pp. 663–682. DOI: 10.1145/3742413.3789223
Model-Based Control for Soft Robots With System Uncertainties and Input Saturation
This article aims at solving challenges regarding accuracy and actuation by proposing a robust model-based strategy for the shape control of soft robots with system uncertainty and input saturation.
Shao, X., Pustina, P., Stölzle, M., Sun, G., De Luca, A., Wu, L., & Della Santina, C., IEEE Transactions on Industrial Electronics, 1–10, 2023. DOI: 10.1109/TIE.2023.3303636
Model-Based Control of Soft Robots: A Survey of the State of the Art and Open Challenges
This work aims to introduce the control theorist perspective to continuum soft robotics.
C. Della Santina, C. Duriez and D. Rus, IEEE Control Systems Magazine, vol. 43, no. 3, pp. 30-65, 2023, DOI: 10.1109/MCS.2023.3253419.
Modelling Handed Shearing Auxetics: Selective Piecewise Constant Strain Kinematics and Dynamic Simulation
This paper proposes two key components extending discrete Cosserat rod model (DCM) to allow for modeling Handed Shearing Auxetics (HSAs) for electrically-actuated continuum soft robots.
M. Stölzle, L. Chin, R. L. Truby, D. Rus and C. D. Santina, 2023 IEEE International Conference on Soft Robotics (RoboSoft), 2023, pp. 1-8, DOI: 10.1109/RoboSoft55895.2023.10121989.
Modular Wavelength Adaptation of the Dynamic Optical MicroEnvironment
This paper presents a modular solution to allow general light sources to be used with the DOME, a powerful and adaptable platform that facilitates the study of light-reactive systems at the microscale.
N. Wijewardhane, M. Uppington, M. How, H. Hauser, E. Piddini and S. Hauert, 2023 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), Abu Dhabi, United Arab Emirates, 2023, pp. 1-6, doi: 10.1109/MARSS58567.2023.10294114.
MultiSTOP: Solving Functional Equations with Reinforcement Learning
This paper develops MultiSTOP, a Reinforcement Learning framework for solving functional equations in physics.
Alessandro Trenta, Davide Bacciu, Andrea Cossu, Pietro Ferrero. ICLR 2024 Workshop on AI4DifferentialEquations In Science. DOI: 10.48550/arXiv.2404.14909
Nanowire Neural Networks for time-series processing
The authors introduce a novel computational framework inspired by the physics of nanowire memristive networks, which is embed into the context of Recurrent Neural Networks (RNNs) for time-series processing.
Veronica Pistolesi, Andrea Ceni, Claudio Gallicchio, Gianluca Milano, Carlo Ricciardi. NeurIPS 2024 Workshop Machine Learning with new Compute Paradigms.
Neural Autoencoder-Based Structure-Preserving Model Order Reduction and Control Design for High-Dimensional Physical Systems
This letter concerns control-oriented and structure-preserving learning of low-dimensional approximations of high-dimensional physical systems, with a focus on mechanical systems.
M. Lepri, D. Bacciu and C. D. Santina, in IEEE Control Systems Letters, vol. 8, pp. 133-138, 2024, DOI: 10.1109/LCSYS.2023.3344286.
NiSNN-A: Noniterative Spiking Neural Network With Attention With Application to Motor Imagery EEG Classification
In this work, the authors combine spiking neural networks (SNNs) and the attention mechanisms for electroencephalogram (EEG) classification, aiming to improve precision and reduce energy consumption.
C. Zhang, W. Pan and C. D. Santina, in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2025.3538335.
