2 citations · 9 across the 19 of their papers we have counts for
4 papers · 1 filter
PiSAs: Benchmarking Contextual Integrity in Multi-User Agentic Systems
Shubham Gupta, Nazanin Mohammadi Sepahvand, Abhinav Kumar +6
As LLM agents evolve from single-user assistants into shared organizational infrastructure, new privacy risks emerge: inappropriate information may not only be exposed through outp…
Dr-CiK: A Benchmark testing Deep Research for Context-Aided Forecasting
Yihong Tang, Andrew Robert Williams, Arjun Ashok +7
Time series forecasting in real-world settings often depends not only on historical observations, but also on external context that must be actively discovered from noisy, heteroge…
Overcoming the Modality Gap in Context-Aided Forecasting
Vincent Zhihao Zheng, Étienne Marcotte, Arjun Ashok +4
Context-aided forecasting (CAF) holds promise for integrating domain knowledge and forward-looking information, enabling AI systems to surpass traditional statistical methods. Howe…
Bound to Disagree: Generalization Bounds via Certifiable Surrogates
Mathieu Bazinet, Valentina Zantedeschi, Pascal Germain
Generalization bounds for deep learning models are typically vacuous, not computable or restricted to specific model classes. In this paper, we tackle these issues by providing new…