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Hierarchical Retrieval at Scale: Bridging Transparency and Efficiency
Information retrieval is a core component of many intelligent systems as it enables conditioning of outputs on new and large-scale …
Overcoming the Modality Gap in Context-Aided Forecasting
Context-aided forecasting (CAF) holds promise for integrating domain knowledge and forward-looking information, enabling AI systems to …
Beyond Naïve Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs
Forecasting in real-world settings requires models to integrate not only historical data but also relevant contextual information, …
GitChameleon: Evaluating AI Code Generation Against Python Library Version Incompatibilities
The rapid evolution of software libraries presents a significant challenge for code generation models, which must adapt to frequent …
How to Train Your LLM Web Agent: A Statistical Diagnosis

Large language model (LLM) agents for web interfaces have advanced rapidly, yet open-source systems still lag behind proprietary …

Beyond Naïve Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs
Forecasting in real-world settings requires models to integrate not only historical data but also relevant contextual information, …
Using Scaling Laws for Data Source Utility Estimation in Domain-Specific Pre-Training
We introduce a framework for optimizing domain-specific dataset construction in foundation model training. Specifically, we seek a …
AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery
We introduce AgentAda, the first LLM-powered analytics agent that can learn and use new analytics skills to extract more specialized …
Adaptive Diffusion Denoised Smoothing : Certified Robustness via Randomized Smoothing with Differentially Private Guided Denoising Diffusion
We propose Adaptive Diffusion Denoised Smoothing, a method for certifying the predictions of a vision model against adversarial …