Resource library
Evaluating Coding Agent Capabilities with Terminal-Bench: Snorkel’s Role in Building the Next Generation Benchmark
Closing the Evaluation Gap in Agentic AI
Benchtalks #1: Alex Shaw (Terminal-Bench, Harbor) – Building the Benchmark Factory
Building FinQA: An Open RL Environment for Financial Reasoning Agents
Strategic dominance in the Indo-Pacific relies on the ability to track and coordinate friendly forces—”blue objects”—with absolute precision. To maintain operational awareness in dynamic and contested environments, the Department of War identified a requirement for adaptable, dual-use technologies that enhance logistics and decision-making resilience.
SlopCodeBench reveals how AI coding agents degrade code quality over time—measuring “slop,” technical debt, and architectural erosion across iterations.
Today, we’re sharing details about the Snorkel Agentic Coding benchmark—a comprehensive evaluation suite designed to test whether agents can handle the full complexity of software engineering work.
This Top 5 Global Telco aimed to evolve its internal billing co-pilot into a customer-facing chatbot capable of serving its global customer base. However, the project stalled at 54% accuracy due to data blind spots and reasoning errors that frustrated efforts to launch.
Our NeurIPS 2025 retrospective The Snorkel AI team We just returned from NeurIPS 2025, and we’re still processing everything we saw. The energy around data-centric AI has never been stronger—and we couldn’t be more grateful to the research community for pushing these ideas forward. The evolution we’ve witnessed When we first brought Snorkel AI research to NeurIPS back in 2019,…
Explores how rubrics support agentic, multi-turn, tool-using, multimodal, and code-generating AI systems, and how they evolve with AI feedback and ensemble evaluation.
TL;DR: We stress-tested the “generate → criticize → improve” loop on 50 visual reasoning tasks. The results were counterintuitive: self-critique acts as a corrosive agent on high-performance tasks, turning 98% accuracy into 57%. Yet, for tasks where models fail completely, it works like magic. This difficulty-dependent behavior poses a critical, hidden risk for RLFT pipelines. The Promise vs. The Reality…
Snorkel Chief Scientist Fred Sala and Kobie Crawford chat with the Terminal-Bench team to unpack the design behind Terminal-Bench 2.0 and the new Harbor framework.
Snorkel AI contributes specialized datasets to Hazy Research’s “Intelligence-per-Watt” study, advancing how efficiently AI turns energy into intelligence.
