Feature-addition benchmark for LLMs and coding agents, evaluated against the Scorch codebase. Each task asks a model to add a feature (or otherwise extend functionality) to Scorch. Success is defined as the full pytest suite (original + any new tests the model adds) passing after the patch is applied inside a Docker container. This is the dataset artifact for the TensorBench paper (NeurIPS 2026 Evaluations & Datasets track, double-blind submission).

At a… See the full description on the dataset page: https://huggingface.co/datasets/tensorbench/tensorbench-1.0.