collaborators

5 papers

eess.SP2026

Wireless Physical-Layer Foundation Models: Architectures, Learning Paradigms, Applications, and Deployment

Mohammad Cheraghinia, Davide Buffelli, Liu Li +6

Foundation models, i.e., large neural networks pretrained on broad unlabeled data and adapted to many downstream tasks, have reshaped natural language processing and computer visio…

stat.ML2026

A Finite Time Analysis of Thompson Sampling for Bayesian Optimization with Preferential Feedback

Joseph Lazzaro, Davide Buffelli, Da-shan Shiu +1

Preference feedback, in the form of pairwise comparisons rather than scalar scores, has seen increasing use in applications such as human-, laboratory-, and expert-in-the-loop desi…

cs.CL2026

Cross-Tokenizer LLM Distillation through a Byte-Level Interface

Avyav Kumar Singh, Yen-Chen Wu, Alexandru Cioba +2

Cross-tokenizer distillation (CTD), the transfer of knowledge from a teacher to a student language model when the two use different tokenizers, remains a largely unsolved problem.…

cs.AI2025

Towards a Foundation Model for Communication Systems

Davide Buffelli, Sowmen Das, Yu-Wei Lin +5

Artificial Intelligence (AI) has demonstrated unprecedented performance across various domains, and its application to communication systems is an active area of research. While cu…

cs.AI2025

Group Think: Multiple Concurrent Reasoning Agents Collaborating at Token Level Granularity

Chan-Jan Hsu, Davide Buffelli, Jamie McGowan +4

Recent advances in large language models (LLMs) have demonstrated the power of reasoning through self-generated chains of thought. Multiple reasoning agents can collaborate to rais…