Resource library

A procedurally generated and programmatically verified benchmark for evaluating spatial reasoning capabilities in LLMs Large language models (LLMs) are showing remarkable results on solving complex reasoning problems across domains—from mathematical proofs and logical puzzles to graduate-level science and engineering questions. On the other hand, their spatial reasoning capabilities are less understood, even though such reasoning underlies many everyday tasks. We…

Forecasting in the real world requires integrating structured time-series data with unstructured textual information, but existing methods are architecturally limited by fixed input/output horizons and are unable to model or quantify uncertainty. We address this challenge by introducing LLM-integrated Bayesian State space models (LBS), a novel probabilistic framework for multimodal temporal forecasting. At a high level, LBS consists of two…

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…

Evaluating the effectiveness of unlearning in large language models (LLMs) remains a key challenge, especially as existing metrics often rely on specific reference outputs. The widely used forget quality metric from the TOFU benchmark compares likelihoods over paraphrased answers but is highly sensitive to the choice of the reference answers, potentially obscuring whether a model has truly forgotten the targeted information. We…

LLM-as-a-judge—often with multiple judges—is now the standard for scalable model evaluation, yet judge biases and correlations can amplify errors. We cast aggregation as inference in a latent-factor Markov random field that jointly models a latent true-quality variable, inter-judge correlations, and confounders (e.g., generation length). We address two key technical challenges—identifiability and learning a higher-rank latent structure—via CARE, a two-stage estimator that…

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.