Resource library
Explore our complete library of resources including blogs, benchmarks, research papers, and more.
Data 2.0 and the research era of AI data
Announcing Snorkel’s $350M fundraise to build the frontier lab for AI data.
Terminal-Bench 4.0: Why Continuous Benchmarks Require Continuous QA
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
Why Frontier Agents Fail Real Engineering Work: Two Terminal-Bench 3.0 Task Deep Dives
Building FinQA: An Open RL Environment for Financial Reasoning Agents
The science of rubric design
Aligning pretrained language models (LMs) is a complex and resource-intensive process, often requiring access to large amounts of ground-truth preference data and substantial compute. Are these costs necessary? That is, it is possible to align using only inherent model knowledge and without additional training? We tackle this challenge with ALIGNEZ, a novel approach that uses (1) self-generated preference data and…
“Recent works often assume that VisionLanguage Model (VLM) representations are based on visual attributes like shape. However, it is unclear to what extent VLMs prioritize this information to represent concepts. We propose Extract and Explore (EX2), a novel approach to characterize important textual features for VLMs. EX2 uses reinforcement learning to align a large language model with VLM preferences and…
Large language models (LLMs), even when specifically trained to process long input contexts, struggle to capture relevant information located in the middle of their input. This phenomenon has been known as the lost-in-themiddle problem. In this work, we make three contributions. First, we set out to understand the factors that cause this phenomenon. In doing so, we establish a connection…
Large language models are prominently used in real-world applications, often tasked with reasoning over large volumes of documents. An exciting development in this space is models boasting extended context capabilities, with some accommodating over 2 million tokens. Such long context model capabilities remain uncertain in production systems, motivating the need to benchmark their performance on real world use cases. We…
Importance: Diagnostic codes are commonly used as inputs for clinical prediction models, to create labels for prediction tasks, and to identify cohorts for multicenter network studies. However, the coverage rates of diagnostic codes and their variability across institutions are underexplored. Objective: Primary objective was to describe lab- and diagnosis-based labels for 7 selected outcomes at three institutions. Secondary objectives were…
Studies rarely use real patient care data for LLM evaluation. Administrative tasks such as generating provider billing codes and writing prescriptions are understudied. Natural Language Processing (NLP)/Natural Language Understanding (NLU) tasks like summarization, conversational dialogue, and translation are infrequently explored. Accuracy is the predominant dimension of evaluation, while fairness, bias and toxicity assessments are neglected. Evaluations in specialized fields, such…
Background: Foundation models hold promise for transforming artificial intelligence (AI) in healthcare by providing modular components that are easily adaptable to downstream healthcare tasks, making AI development more scalable and cost-effective. Foundation models for structured electronic health records (EHR), trained on coded medical records from millions of patients, demonstrated benefits including increased performance with fewer training labels, and improved robustness…
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