9 papers
ScalableRAG: High-Quality RAG at Zero Ingestion Cost
Hilaf Hasson, Aditya Chakravarty, Jayant Thomas +1
Recent advances in RAG aim to optimize for performance by paying high ingestion costs for knowledge ingestion: building knowledge graphs or extracting SQL tables. In this work we s…
RIMRULE: Improving Tool-Using Language Agents via MDL-Guided Rule Learning
Xiang Gao, Yuguang Yao, Qi Zhang +5
Large language models (LLMs) often struggle to use tools reliably in domain-specific settings, where APIs may be idiosyncratic, under-documented, or tailored to private workflows.…
UA-DCM: Uncertainty-aware Causal Decision Making via Effect Bound Decomposition
Md Musfiqur Rahman, Ziwei Jiang, Hilaf Hasson +1
Causal inference from observational data can provide strong evidence for finding the best action in a decision-making scenario without having to perform expensive randomized trials…
Executable Schema Contracts: From Automatic Ingestion to Multi-Source Retrieval
Padmaja Jonnalagedda, Yuguang Yao, Xiang Gao +2
Real-world data spans tables, documents, and semi-structured files with implicit semantics. Querying this data requires integrating evidence across inconsistent schemas and formats…
OMAC: A Holistic Optimization Framework for LLM-Based Multi-Agent Collaboration
Shijun Li, Hilaf Hasson, Joydeep Ghosh
Agents powered by advanced large language models (LLMs) have demonstrated impressive capabilities across diverse complex applications. Recently, Multi-Agent Systems (MAS), wherein…
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting
Hilaf Hasson, Danielle C. Maddix, Yuyang Wang +2
Ensembling is among the most popular tools in machine learning (ML) due to its effectiveness in minimizing variance and thus improving generalization. Most ensembling methods for b…