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.
An Untrained Neural Model for Fast and Accurate Graph Classification
This paper aims to explore a simple form of a randomized graph neural network inspired by the success of randomized convolutions in the 1-dimensional domain.
Navarin, N., Pasa, L., Gallicchio, C., Sperduti, A. (2023). In Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14257. Springer, Cham. doi: 10.1007/978-3-031-44216-2_23
Anti-Symmetric DGN: a stable architecture for Deep Graph Networks
In this work, the authors present Anti-Symmetric Deep Graph Networks (A-DGNs), a framework for stable and non-dissipative DGN design, conceived through the lens of ordinary differential equations.
Gravina, A., Bacciu, D., & Gallicchio, C. (2022). Proceedings of the 11th International Conference on Learning Representations (ICLR). doi: 10.48550/arXiv.2210.09789.
Automating the Evaluation of the Scalability, Flexibility, and Robustness of Collective Behaviors for Robot Swarms
In this paper, the authors use recently proposed experimental protocols to evaluate these properties in various typical collective behaviors for robot swarms.
G. M. Madroñero Pachajoa, W. Achicanoy and D. G. Ramos, 2024 Brazilian Symposium on Robotics (SBR) and 2024 Workshop on Robotics in Education (WRE), Goiania, Brazil, 2024, pp. 144-149, doi: 10.1109/SBR/WRE63066.2024.10837963.
Awareness in Robotics: An Early Perspective from the Viewpoint of the EIC Pathfinder Challenge “Awareness Inside”
This paper summarizes and discusses the projects funded by the EIC Pathfinder Challenge “Awareness Inside” call within Horizon Europe, designed specifically for fostering research on natural and synthetic awareness.
Della Santina, C. et al. (2024). In European Robotics Forum 2024. ERF 2024. Springer Proceedings in Advanced Robotics, vol 32. doi: 10.1007/978-3-031-76424-0_20
Back to Bee-sics: Learning Information Sharing Strategies for Robot Swarms Through the Hive
This study uses a learning-based approach to optimise information sharing in a hybrid robot swarm, where each robot maintains local autonomy, but information is shared via a central repository.
Henry Hickson, Sabine Hauert, Alex Mavromatis (2025). Proceedings of the ALIFE 2025: Ciphers of Life: Proceedings of the Artificial Life Conference 2025. Kyoto, Japan. (pp. 78). DOI: 10.1162/ISAL.a.906
Benchmarking Nonlinear Readouts in Linear Reservoir Networks
This paper systematically benchmarks a spectrum of nonlinear readouts within linear RC frameworks.
Lagomarsini, G., Ceni, A., Gallicchio, C. (2026). ICANN 2025 International Workshops and Special Sessions. ICANN 2025. Lecture Notes in Computer Science, vol 16072. DOI: 10.1007/978-3-032-04552-2_17
BerryTwist: A Twisting-Tube Soft Robotic Gripper for Blackberry Harvesting
This paper introduces BerryTwist, a prototype robotic gripper specifically designed for blackberry harvesting.
J. F. Elfferich, E. Shahabi, C. D. Santina and D. Dodou, in IEEE Robotics and Automation Letters, vol. 10, no. 1, pp. 429-435, Jan. 2025, doi: 10.1109/LRA.2024.3505813.
Building Trustworthiness by Minimizing the Sim-to-Real Gap in Fault Detection for Robot Swarms
In this work, the authors implement metric extraction in a real-world environment and evaluate whether the extracted metrics can overcome the “sim-to-real gap”
Suet Lee and Sabine Hauert. 2023. In Proceedings of the First International Symposium on Trustworthy Autonomous Systems (TAS '23). Association for Computing Machinery, New York, NY, USA, Article 47, 1–3. doi: 10.1145/3597512.3597527
Calibration of Continual Learning Models
This paper provides the first empirical study of the behavior of calibration approaches in CL, showing that CL strategies do not inherently learn calibrated models.
L. Li, E. Piccoli, A. Cossu, D. Bacciu and V. Lomonaco, 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA, 2024, pp. 4160-4169, doi: 10.1109/CVPRW63382.2024.00419.
Can You Hear Me Now? A Benchmark for Long-Range Graph Propagation
This paper introduces ECHO (Evaluating Communication over long HOps), a novel benchmark specifically designed to rigorously assess the capabilities of GNNs in handling very long-range graph propagation.
Luca Miglior, Matteo Tolloso, Alessio Gravina, Davide Bacciu, in Proc. International Conference on Learning Representations, 2026.
Cellular Au-Tonnetz: A Unified Audio-Visual MIDI Generator Using Tonnetz, Cellular Automata, and IoT
This paper presents a detailed description of an innovative tool for music creation that merges sound and light through a unified system.
Didiot-Cook, T. (2025). Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2025. Lecture Notes in Computer Science, vol 15611. DOI: 10.1007/978-3-031-90167-6_4
Co-perceiving: Bringing the social into perception
In this comprehensive review, the authors advocate for a broader and more mechanistic understanding of the phenomenon called co-perception.
Deroy, O., Longin, L., & Bahrami, B. (2024). WIREs Cognitive Science, e1681. doi: 10.1002/wcs.1681
Collective intelligence in clinical medicine: what works, what fails, and how collaboration with AI really helps
This paper defines collective intelligence in medicine and explain why healthcare is a uniquely important domain for its study, given the combination of high complexity and high stakes.
Ophelia Deroy, Bahador Bahrami, La Presse Médicale, v. 55, 2026. DOI: 10.1016/j.lpm.2026.104362.
ComPhy: Composing Physical Models with end-to-end Alignment
This paper introduces ComPhy (CP), a novel modular framework designed to leverage the inherent physical structure of the problem to solve systems of PDEs.
Alessandro Trenta, Andrea Cossu, Davide Bacciu, "ComPhy: Composing Physical Models with end-to-end Alignment", in Proc. International Conference on Learning Representations, 2026.
Consensus in the weighted voter model with noise-free and noisy observations
This work presents an exact finite-population analysis of the best-of-two model on complete as well as regular network topologies.
Ganesh, A., Hauert, S. & Valla, E. Swarm Intell 19, 173–214 (2025). DOI: 10.1007/s11721-025-00248-z
Contact-Aware Safety in Soft Robots Using High-Order Control Barrier and Lyapunov Functions
This letter introduces a comprehensive framework that enforces strict contact force limits across the entire soft-robot body during environmental interactions.
K. Wong, M. Stölzle, W. Xiao, C. D. Santina, D. Rus and G. Zardini, in IEEE Robotics and Automation Letters, vol. 10, no. 12, pp. 12485-12492, Dec. 2025, doi: 10.1109/LRA.2025.3621965
Continual Learning of Physical Systems via Derivative Distillation
This work adopts Derivative Distillation, a distillation-based approach that leverages model derivatives as a compact representation of knowledge and integrates it within a physics-informed learning framework.
Claudia Gentili, Alessandro Trenta, Andrea Cossu, Davide Bacciu, in Proc. Forty-third International Conference on Machine Learning, 2026.
Continual pre-training mitigates forgetting in language and vision
This paper investigates the characteristics of the Continual Pre-Training scenario, where a model is continually pre-trained on a stream of incoming data and only later fine-tuned to different downstream tasks.
Andrea Cossu, Antonio Carta, Lucia Passaro, Vincenzo Lomonaco, Tinne Tuytelaars, Davide Bacciu, Neural Networks, 179, 2024, 106492, doi: 10.1016/j.neunet.2024.106492.
