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This Microsoft Research paper introduces a randomized scheduling technique designed to provide probabilistic guarantees for uncovering bugs in software systems. Published for the ASPLOS conference, it focuses on systematic fault detection through algorithmic randomness.
This paper introduces DataDignity, a framework and benchmark (FakeWiki) for pinpoint provenance, aiming to identify the specific training data sources that support an LLM's response. It proposes ScoringModel and SteerFuse methods to improve attribution accuracy over standard retrieval baselines.
This paper introduces AgenticRAG, a framework from Microsoft that enhances enterprise knowledge base retrieval by equipping LLMs with tools for iterative search, document navigation, and analysis. It demonstrates significant improvements in recall and factuality over standard RAG pipelines on multiple benchmarks.
Microsoft Research introduced Agentic-iModels, a framework where coding agents evolve scikit-learn regressors optimized for LLM interpretability rather than human readability, outperforming traditional interpretable ML methods across 65 datasets.
Microsoft Research releases Skala, a deep-learning exchange-correlation functional for DFT that achieves 2.8 kcal/mol accuracy on GMTKN55 at semi-local cost, outperforming traditional functionals across broad chemistry benchmarks.