10 citations · 18 across the 18 of their papers we have counts for
19 papers
TRACES: Tagging Reasoning Steps for Adaptive Cost-Efficient Early-Stopping
Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo +1
The field of Language Reasoning Models (LRMs) has been very active over the past few years with advances in training and inference techniques enabling LRMs to reason longer, and mo…
Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models
Yannis Belkhiter, Giulio Zizzo, Sergio Maffeis +2
The growth of agentic AI has drawn significant attention to function calling Large Language Models (LLMs), which are designed to extend the capabilities of AI-powered system by inv…
msData: A Millisecond-Resolution Network Dataset for Advancing Time Series Foundation Models
Subina Khanal, Seshu Tirupathi, Merim Dzaferagic +2
Time series foundation models (TSFMs) require diverse, real-world datasets to adapt across varying domains and temporal frequencies. However, current large-scale datasets predomina…
Shapelets-Enriched Selective Forecasting using Time Series Foundation Models
Shivani Tomar, Seshu Tirupathi, Elizabeth Daly +1
Time series foundation models have recently gained a lot of attention due to their ability to model complex time series data encompassing different domains including traffic, energ…
Step-Tagging: Toward controlling the generation of Language Reasoning Models through step monitoring
Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo +1
The field of Language Reasoning Models (LRMs) has been very active over the past few years with advances in training and inference techniques enabling LRMs to reason longer, and mo…
Dynamic Features Adaptation in Networking: Toward Flexible training and Explainable inference
Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo +2
As AI becomes a native component of 6G network control, AI models must adapt to continuously changing conditions, including the introduction of new features and measurements driven…
Interpreting LLM-as-a-Judge Policies via Verifiable Global Explanations
Jasmina Gajcin, Erik Miehling, Rahul Nair +3
Using LLMs to evaluate text, that is, LLM-as-a-judge, is increasingly being used at scale to augment or even replace human annotations. As such, it is imperative that we understand…
Pre-Hoc Predictions in AutoML: Leveraging LLMs to Enhance Model Selection and Benchmarking for Tabular datasets
Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo +2
The field of AutoML has made remarkable progress in post-hoc model selection, with libraries capable of automatically identifying the most performing models for a given dataset. Ne…
GAF-Guard: An Agentic Framework for Risk Management and Governance in Large Language Models
Seshu Tirupathi, Dhaval Salwala, Elizabeth Daly +1
As Large Language Models (LLMs) continue to be increasingly applied across various domains, their widespread adoption necessitates rigorous monitoring to prevent unintended negativ…
AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources
Frank Bagehorn, Kristina Brimijoin, Elizabeth M. Daly +17
The rapid evolution of generative AI has expanded the breadth of risks associated with AI systems. While various taxonomies and frameworks exist to classify these risks, the lack o…
Usage Governance Advisor: From Intent to AI Governance
Elizabeth M. Daly, Sean Rooney, Seshu Tirupathi +9
Evaluating the safety of AI Systems is a pressing concern for organizations deploying them. In addition to the societal damage done by the lack of fairness of those systems, deploy…
Decentralized Multi-Party Multi-Network AI for Global Deployment of 6G Wireless Systems
Merim Dzaferagic, Marco Ruffini, Nina Slamnik-Krijestorac +21
Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will…
Domain Adaptation for Time series Transformers using One-step fine-tuning
Subina Khanal, Seshu Tirupathi, Giulio Zizzo +2
The recent breakthrough of Transformers in deep learning has drawn significant attention of the time series community due to their ability to capture long-range dependencies. Howev…
Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks
Francesco Fusco, Bradley Eck, Robert Gormally +2
The transition away from carbon-based energy sources poses several challenges for the operation of electricity distribution systems. Increasing shares of distributed energy resourc…
Perfusion Quantification from Endoscopic Videos: Learning to Read Tumor Signatures
Sergiy Zhuk, Jonathan P. Epperlein, Rahul Nair +4
Intra-operative identification of malignant versus benign or healthy tissue is a major challenge in fluorescence guided cancer surgery. We propose a perfusion quantification method…
Scalable Deployment of AI Time-series Models for IoT
Bradley Eck, Francesco Fusco, Robert Gormally +2
IBM Research Castor, a cloud-native system for managing and deploying large numbers of AI time-series models in IoT applications, is described. Modelling code templates, in Python…
AI Modelling and Time-series Forecasting Systems for Trading Energy Flexibility in Distribution Grids
Bradley Eck, Francesco Fusco, Robert Gormally +2
We demonstrate progress on the deployment of two sets of technologies to support distribution grid operators integrating high shares of renewable energy sources, based on a market…
Castor: Contextual IoT Time Series Data and Model Management at Scale
Bei Chen, Bradley Eck, Francesco Fusco +4
We demonstrate Castor, a cloud-based system for contextual IoT time series data and model management at scale. Castor is designed to assist Data Scientists in (a) exploring and ret…
Power Systems Data Fusion based on Belief Propagation
Francesco Fusco, Seshu Tirupathi, Robert Gormally
The increasing complexity of the power grid, due to higher penetration of distributed resources and the growing availability of interconnected, distributed metering devices re- qui…