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Explore our complete library of resources including blogs, benchmarks, research papers, and more.

Image for Grok 4.5 Testing Results: How SpaceXAI’s New Model Performs on Real Professional Work
Blog

Grok 4.5 Testing Results: How SpaceXAI’s New Model Performs on Real Professional Work

Announcing a $3M commitment to launch Open Benchmarks Grants
July 8, 2026
Image for Why coding agents need better data, evals, and environments
Blog

Why coding agents need better data, evals, and environments

Announcing a $3M commitment to launch Open Benchmarks Grants
May 11, 2026
Image for Closing the Evaluation Gap in Agentic AI
Blog

Closing the Evaluation Gap in Agentic AI

Announcing a $3M commitment to launch Open Benchmarks Grants

February 11, 2026
Image for Evaluating coding agent capabilities with Terminal-Bench: Snorkel’s role in building the next generation benchmark
Blog

Evaluating coding agent capabilities with Terminal-Bench: Snorkel’s role in building the next generation benchmark

Announcing a $3M commitment to launch Open Benchmarks Grants
September 30, 2025
Image for Building FinQA: An Open RL Environment for Financial Reasoning Agents
Blog

Building FinQA: An Open RL Environment for Financial Reasoning Agents

Announcing a $3M commitment to launch Open Benchmarks Grants
March 30, 2026
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Blog

The science of rubric design

Announcing a $3M commitment to launch Open Benchmarks Grants
September 11, 2025
Image for Benchtalks #3: We taught AI everything except how to learn
Blog

Benchtalks #3: We taught AI everything except how to learn

Featuring Parth Asawa (Continual Learning Bench)

June 25, 2026
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Out of distribution blindness: why to fix it and how energy can help
Blog
Out of distribution blindness: why to fix it and how energy can help

Sharon Li is an assistant professor at the University of Wisconsin-Madison. She presented “Detecting Data Distributional Shift: Challenges and Opportunities” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. The talk covered a novel approach for handling out-of-distribution objects.

May 03, 2023
Learn more about Out of distribution blindness: why to fix it and how energy can help
Evaluation of Feature Selection Methods for Preserving Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine
Research Paper
Evaluation of Feature Selection Methods for Preserving Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine
May 01, 2023

J. Lemmon, et al.

Learn more about Evaluation of Feature Selection Methods for Preserving Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine
Debugging data to build better and more fair ML applications
Blog
Debugging data to build better and more fair ML applications

Dr. Ce Zhang is an associate professor in Computer Science at ETH Zürich. He presented “Building Machine Learning Systems for the Era of Data-Centric AI” at Snorkel AI’s The Future of Data-Centric AI event in 2022.

Apr 28, 2023
Learn more about Debugging data to build better and more fair ML applications
Machine learning on Kubernetes: wisdom learned at Snorkel AI
Blog
Machine learning on Kubernetes: wisdom learned at Snorkel AI

Snorkel Flow handles intense machine learning workloads, and we’ve built our infrastructure on a foundation of Kubernetes—which was not designed with machine learning in mind.

Apr 27, 2023
Learn more about Machine learning on Kubernetes: wisdom learned at Snorkel AI
Harvard professor: DataPerf and AI’s need for data benchmarks
Blog
Harvard professor: DataPerf and AI’s need for data benchmarks

Harvard Professor Vijay Janapa Reddi’s presentation: “DataPerf: Benchmarks for data” from Snorkel AI’s 2022 Future of Data-Centric AI event.

Apr 25, 2023
Learn more about Harvard professor: DataPerf and AI’s need for data benchmarks
Learning to Compose Soft Prompts for Compositional Zero-Shot Learning
We introduce compositional soft prompting (CSP), a parameter-efficient learning technique to improve the zero-shot compositionality of large-scale pretrained vision-language models (VLMs) like CLIP. We develop CSP for compositional zero-shot learning, the task of predicting unseen attribute-object compositions (e.g., old cat and young tiger). VLMs have a flexible text encoder that can represent arbitrary classes as natural language prompts but they often underperform taskspecific architectures on the compositional zero-shot benchmark datasets. CSP treats the attributes and objects that define classes as learnable tokens of vocabulary. During training, the vocabulary is tuned to recognize classes that compose tokens in multiple ways (e.g.,...
Research Paper
Learning to Compose Soft Prompts for Compositional Zero-Shot Learning

We introduce compositional soft prompting (CSP), a parameter-efficient learning technique to improve the zero-shot compositionality of large-scale pretrained vision-language models (VLMs) like CLIP. We develop CSP for compositional zero-shot learning, the task of predicting unseen attribute-object compositions (e.g., old cat and young tiger). VLMs have a flexible text encoder that can represent arbitrary classes as natural language prompts but they…

Apr 24, 2023

N. Nayak et al.

Learn more about Learning to Compose Soft Prompts for Compositional Zero-Shot Learning
Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes
A long standing goal of the data management community is to develop general, automated systems that ingest semi-structured documents and output queryable tables without human effort or domain specific customization. Given the sheer variety of potential documents, state-of-the art systems make simplifying assumptions and use domain specific training. In this work, we ask whether we can maintain generality by using large language models (LLMs). LLMs, which are pretrained on broad data, can perform diverse downstream tasks simply conditioned on natural language task descriptions. We propose and evaluate EVAPORATE, a simple, prototype system powered by LLMs. We identify two fundamentally different...
Research Paper
Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes

A long standing goal of the data management community is to develop general, automated systems that ingest semi-structured documents and output queryable tables without human effort or domain specific customization. Given the sheer variety of potential documents, state-of-the art systems make simplifying assumptions and use domain specific training. In this work, we ask whether we can maintain generality by using…

Apr 21, 2023

S. Arora, et al.

Learn more about Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes
AI for banking in the era of ChatGPT
Blog
AI for banking in the era of ChatGPT

Forward-looking companies in finance, including banks, have looked to technology to meet challenges and are reaping the rewards of doing so.

Apr 20, 2023
Learn more about AI for banking in the era of ChatGPT
Uniphore chooses Snorkel Flow to accelerate conversational AI
Blog
Uniphore chooses Snorkel Flow to accelerate conversational AI

Uniphore, a conversational AI and automation leader, has chosen Snorkel’s data-centric AI platform to accelerate AI development.

Apr 19, 2023
Learn more about Uniphore chooses Snorkel Flow to accelerate conversational AI
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