Resource library
Explore our complete library of resources including blogs, benchmarks, research papers, and more.
Grok 4.5 Testing Results: How SpaceXAI’s New Model Performs on Real Professional Work
Why coding agents need better data, evals, and environments
Closing the Evaluation Gap in Agentic AI
Announcing a $3M commitment to launch Open Benchmarks Grants
Evaluating coding agent capabilities with Terminal-Bench: Snorkel’s role in building the next generation benchmark
Building FinQA: An Open RL Environment for Financial Reasoning Agents
The science of rubric design
Presenting Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules.
This paper proposes a universal technique that enables weak supervision over any label type while still offering desirable properties, including practical flexibility, computational efficiency, and theoretical guarantees.
This paper showcases how using a data-centric approach to generate high-quality training data at massive scale to improve the zero-shot abilities of that model.
This paper extends the scope of usable sources in WS, by formulating Weak Indirect Supervision (WIS), a new research problem for automatically synthesizing training labels based on indirect supervision sources that have different output label spaces.
The Future of Data-Centric AI Talk Series Background Chelsea Finn is an assistant professor of computer science and electrical engineering at Stanford University, whose research has been widely recognized, including in the New York Times and MIT Technology Review. In this talk, Chelsea talks about algorithms that use data from tasks you are interested in and data from other tasks….
This paper introduces the Structured State Space sequence model (s4), which uses a new parameterization for the state-space model to improve long-range dependency handling both mathematically and empirically.
This work enables users to create partial labelers that output subsets of possible class labels would greatly expand the expressivity of programmatic weak supervision.
The future of data-centric AI talk series Background Anima Anandkumar holds dual positions in academia and industry. She is a Bren professor at Caltech and the director of machine learning research at NVIDIA. Anima also has a long list of accomplishments ranging from the Alfred P. Sloan scholarship to the prestigious NSF career award and many more. She recently joined…
