Algorithmic Research Group

We've set up @AISecurityInst's Inspect platform as a lightweight remote eval service: Postgres-backed job queue, Dockerized API + worker pool, remote submission, and the existing Inspect web UI for viewing runs. It keeps the core Inspect workflow intact, but makes it practical to run many evals on a shared host. It acts like @METR_Evals vivaria, but has Inspects awesome tooling. Link below:

We're releasing 'ai-sft' on @huggingface: a 34GB supervised fine-tuning dataset for training models on AI research tasks. 2.7M examples spanning research code generation, scientific QA, and technical problem solving, built from our research-focused data collections. Each example includes structured fields for task family, grounded context, source tracing, loss weighting, and quality flags. Train/val splits included. Link below:

We're releasing 's2orc-safety' on @huggingface: a AI safety slice of our s2orc-enriched dataset with 16,806 papers across jailbreaks, prompt injection, red teaming, model security, privacy, robustness, alignment, and more. Each paper is enriched with structured fields for reproducibility, safety taxonomy, experimental details, practicality, normalized model/dataset/metric names, code-link metadata, and more. Link below:

We've added a UK AI Safety Institute (@AISafetyInst) inspect variant to make it easier to run DMLB. Link below:

We're releasing S2ORC CS Enriched, a dataset of 1.1 million computer science papers from Semantic Scholar's S2ORC corpus with LLM-generated enrichment fields added to every row. The base dataset has the full paper text, abstracts, authors, references, citation counts, and venue metadata. We added structured enrichment columns on top: paper summaries, classification, methods used, results, models, datasets, metrics, limitations, and GPU compute details where reported. The enrichment makes it possible to do things that are hard with raw paper text alone, like filtering for papers that used a specific method, or finding papers that report GPU hours, or building training data for models that need to understand the structure of research papers rather than just their text. 1,118 parquet files, 44 GB total. Available on @huggingface huggingface.co/datasets/Algo…

We're excited to release HF agent - a multi-agent @huggingface model-selection and fine-tuning system that investigates current options, searches the web and Hugging Face, and produces Markdown recommendation reports with citations and code snippets. Link below