From the 1 of 14 linked papers with an AI index.
14 papers
Overcoming the Modality Gap in Context-Aided Forecasting
Vincent Zhihao Zheng, Ãtienne Marcotte, Arjun Ashok +4
The paper introduces a semi‑synthetic data augmentation technique to create high‑quality contextual information for time‑series forecasting, producing a 7 million‑sample dataset (C…
Beyond Naïve Prompting: Strategies for Improved Context-aided Forecasting with LLMs
Arjun Ashok, Andrew Robert Williams, Vincent Zhihao Zheng +5
Real-world forecasting requires models to integrate not only historical data but also relevant contextual information provided in textual form. While large language models (LLMs) s…
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…
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 Testbed for Foresight-Driven Agents
Yihong Tang, Andrew Robert Williams, Arjun Ashok +6
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…
Hierarchical Retrieval at Scale: Bridging Transparency and Efficiency
Shubham Gupta, Zichao Li, Tianyi Chen +4
Information retrieval is a core component of many intelligent systems as it enables conditioning of outputs on new and large-scale datasets. While effective, the standard practice…