arXiv subjects

Franco Galante

Publications and source records attributed to Franco Galante.

7 recordsLinked to original sources

arXiv2025

A Network Arena for Benchmarking AI Agents on Network Troubleshooting

Agentic systems, powered by Large Language Models (LLMs), assist network engineers with network configuration synthesis and network troubleshooting tasks. For network troubleshooting, progress is hindered by the absence of standardized and accessible benchmarks for evaluating LLM agents in dynamic network settings at low operational effort. We present NIKA, the largest public benchmark to date for LLM-driven network incident diagnosis and troubleshooting. NIKA targets both domain experts and especially AI researchers alike, providing zero-effort replay of real-world network scenarios, and establishing well-defined agent-network interfaces for quick agent prototyping. NIKA comprises hundreds of curated network incidents, spanning five network scenarios, from data centers to ISP networks, and covers 54 representative network issues. Lastly, NIKA is modular and extensible by design, offering APIs to facilitate the integration of new network scenarios and failure cases. We evaluate state-of-the-art LLM agents on NIKA and find that while larger models succeed more often in detecting network issues, they still struggle to localize faults and identify root causes. NIKA is open-source and available to the community: https://github.com/sands-lab/nika.

cs.NI↗

arXiv2025

Towards a Playground to Democratize Experimentation and Benchmarking of AI Agents for Network Troubleshooting

Recent research has demonstrated the effectiveness of Artificial Intelligence (AI), and more specifically, Large Language Models (LLMs), in supporting network configuration synthesis and automating network diagnosis tasks, among others. In this preliminary work, we restrict our focus to the application of AI agents to network troubleshooting and elaborate on the need for a standardized, reproducible, and open benchmarking platform, where to build and evaluate AI agents with low operational effort.

cs.NI↗

arXiv2025

Dominance or Fair Play in Social Networks? A Model of Influencer Popularity Dynamic

This paper presents a data-driven mean-field approach to model the popularity dynamics of users seeking public attention, i.e., influencers. We propose a novel analytical model that integrates individual activity patterns, expertise in producing viral content, exogenous events, and the platform's role in visibility enhancement, ultimately determining each influencer's success. We analytically derive sufficient conditions for system ergodicity, enabling predictions of popularity distributions. A sensitivity analysis explores various system configurations, highlighting conditions favoring either dominance or fair play among influencers. Our findings offer valuable insights into the potential evolution of social networks towards more equitable or biased influence ecosystems.

cs.SI↗

arXiv2025

Information Retrieval in the Age of Generative AI: The RGB Model

The advent of Large Language Models (LLMs) and generative AI is fundamentally transforming information retrieval and processing on the Internet, bringing both great potential and significant concerns regarding content authenticity and reliability. This paper presents a novel quantitative approach to shed light on the complex information dynamics arising from the growing use of generative AI tools. Despite their significant impact on the digital ecosystem, these dynamics remain largely uncharted and poorly understood. We propose a stochastic model to characterize the generation, indexing, and dissemination of information in response to new topics. This scenario particularly challenges current LLMs, which often rely on real-time Retrieval-Augmented Generation (RAG) techniques to overcome their static knowledge limitations. Our findings suggest that the rapid pace of generative AI adoption, combined with increasing user reliance, can outpace human verification, escalating the risk of inaccurate information proliferation across digital resources. An in-depth analysis of Stack Exchange data confirms that high-quality answers inevitably require substantial time and human effort to emerge. This underscores the considerable risks associated with generating persuasive text in response to new questions and highlights the critical need for responsible development and deployment of future generative AI tools.

cs.IR↗

arXiv2025

Scalable Decentralized Algorithms for Online Personalized Mean Estimation

In numerous settings, agents lack sufficient data to directly learn a model. Collaborating with other agents may help, but it introduces a bias-variance trade-off, when local data distributions differ. A key challenge is for each agent to identify clients with similar distributions while learning the model, a problem that remains largely unresolved. This study focuses on a simplified version of the overarching problem, where each agent collects samples from a real-valued distribution over time to estimate its mean. Existing algorithms face impractical space and time complexities (quadratic in the number of agents A). To address scalability challenges, we propose a framework where agents self-organize into a graph, allowing each agent to communicate with only a selected number of peers r. We introduce two collaborative mean estimation algorithms: one draws inspiration from belief propagation, while the other employs a consensus-based approach, with complexity of O( r |A| log |A|) and O(r |A|), respectively. We establish conditions under which both algorithms yield asymptotically optimal estimates and offer a theoretical characterization of their performance.

cs.LG↗

arXiv2023

Planning interventions in a controlled pandemic: the COVID-19 case

Restrictions on social and economic activities, as well as vaccinations, have been a key intervention in containing the COVID-19 epidemic. Our work focuses on better understanding the options available to policymakers under the conditions and uncertainties created by the onset of a new pandemic. More precisely, we focus on two control strategies. The first aims to control the rate of new infections to prevent congestion of the health care system. The latter directly controls hospitalizations and intensive care units (ICUs) occupation. By a first-order analysis, we show that, on the one hand, due to the difficulty in contact tracing and the lack of accurate information, controlling the transmission rate may be difficult, leading to instability. On the other hand, although hospitalizations and ICUs are easily accessible and less noisy than the rate of new infections, a delay is introduced in the control loop, which may endanger system stability. Our framework allows assessing the impact on economic and social costs of the above strategies in a scenario enriched by: i) population heterogeneity in terms of mortality rate and risk exposure, ii) closed-loop control of the epidemiological curve, and iii) progressive vaccination of individuals.

math.OC↗

arXiv2023

Modeling communication asymmetry and content personalization in online social networks

The increasing popularity of online social networks (OSNs) attracted growing interest in modeling social interactions. On online social platforms, a few individuals, commonly referred to as influencers, produce the majority of content consumed by users and hegemonize the landscape of the social debate. However, classical opinion models do not capture this communication asymmetry. We develop an opinion model inspired by observations on social media platforms {with two main objectives: first, to describe this inherent communication asymmetry in OSNs, and second, to model the effects of content personalization. We derive a Fokker-Planck equation for the temporal evolution of users' opinion distribution and analytically characterize the stationary system behavior. Analytical results, confirmed by Monte-Carlo simulations, show how strict forms of content personalization tend to radicalize user opinion, leading to the emergence of echo chambers, and favor structurally advantaged influencers. As an example application, we apply our model to Facebook data during the Italian government crisis in the summer of 2019. Our work provides a flexible framework to evaluate the impact of content personalization on the opinion formation process, focusing on the interaction between influential individuals and regular users. This framework is interesting in the context of marketing and advertising, misinformation spreading, politics and activism.

cs.SI↗