Research

Snorkel’s “Trusted Scale” philosophy Welcome to Part 4 of Snorkel AI’s rubric series. In previous posts, we explored how rubrics enable structured evaluation (Part 1), the spectrum of rubric types and use cases (Part 2), and the science behind designing and validating them (Part 3). In this latest installment, we pull back the curtain on how Snorkel puts these principles

In recent months, there has been increasing interest in the area of multi-agent systems and how they can be used to solve more complex tasks than a single agent could accomplish on its own. The topic is particularly interesting and raises several questions and ideas to consider: Anthropic’s blog post about how they architected a multi-agent deep research system is

Terminal-Bench, developed through a collaboration between Stanford University and Laude Institute, has quickly become the gold standard benchmark for evaluating AI agent capabilities in a command line environment. This comprehensive evaluation framework measures how effectively AI agents can perform complex, real-world tasks within terminal environments. At Snorkel AI, we’re excited to share that we’re one of the top collaborators contributing

Behind every AI benchmark is a hidden choice: how to read the model’s answers. That choice—parsing—can quietly tilt results more than the model itself. Parsing is where we take an AI system’s raw response and extract the “answer” we use for scoring. It sounds mechanical, but as our research shows, the choice of parser can dramatically change measured accuracy. In

Part 3 of our rubric series explains the science of rubric design. We show why rubrics should be treated like models—structured, measured, and iterated—to maximize objective alignment and inter-rater agreement. Learn how to choose hierarchy and scale points, track agreement (IAA) and LLMAJ alignment, and refine with domain experts, with examples like PaperBench and HealthBench.

Rubrics turn fuzzy “good vs. bad” into measurable criteria for GenAI. In Part 2, we map what to measure (granularity and dataset-level vs instance-specific), where to measure (process vs outcome), and how to measure (humans, LLM-as-judge, code, reward models)—with examples like HHH, FLASK, HealthBench, and PaperBench.

Rubrics aren’t just for evaluation—they’re a blueprint for better data annotation. In this post, we explore how structured rubrics enable scalable, high-quality labeling and evaluation of GenAI systems. Learn how Snorkel and leading labs use rubrics to align human and automated judgment and accelerate trusted AI development.

We’re taking a look at the research paper, LLMs can easily learn to reason from demonstration (Li et al., 2025), in this week’s community research spotlight. It focuses on how the structure of reasoning traces impacts distillation from models such as DeepSeek R1. What’s the big idea regarding LLM reasoning distillation? The reasoning capabilities of powerful models such as DeepSeek

Learn how ARR improves QA accuracy in LLMs through intent analysis, retrieval, and reasoning. Is intent the key to smarter AI? Explore ARR results!

Snorkel researchers devised a new way to evaluate long context models and address their “lost-in-the-middle” challenges with mediod voting.

ROBOSHOT acts like a lens on foundation models and improves their zero-shot performance without additional fine-tuning.

Microsoft infrastructure facilitates Snorkel AI research experiments, including our recent high rank on the AlpacaEval 2.0 LLM leaderboard.

Humans learn tasks better when taught in a logical order. So do LLMs. Researchers developed a way to exploit this tendency called “Skill-it!”

Training large language models is a multi-layered stack of processes, each with its unique role and contribution to the model’s performance.

Low-rank adaptation (LoRA) lets data scientists customize GenAI models like LLMs faster than traditional full fine-tuning methods.