tensorbench/tensorbench-1.0

TensorBench

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 glance

Tasks	199 (194 feature-addition, 5 refactor)
Target codebase	bobbyyyan/scorch — a sparse+dense PyTorch compiler
Base commits	5 distinct SHAs across the bench branch
Language	Python (with C++ extension)
Test runner	pytest -v inside Docker

Files

File	Purpose
tensorbench.json	Task list. JSON array of 199 task records.
Dockerfile	Eval image: python:3.11-slim + Scorch's C++ build deps + the upstream bench clone.
run_tests.sh	Container CMD: rebuilds the C++ extension and runs pytest.
croissant.json	Croissant metadata (core + RAI fields).

Task schema

Each record in tensorbench.json has:

Field	Type	Description
instance_id	string	e.g. bobbyyyan__scorch-feature_kernel_fusion. The feature_* / refactor_* suffix follows the original taxonomy; semantics for both is feature-addition.
repo_id	string	bobbyyyan__scorch for every task.
repo_url	string	Upstream Scorch URL.
base_commit	string	The SHA the task is anchored at. The harness git reset --hards to this before applying patches.
language	string	python.
setup_commands	list[string]	Container-side setup hooks.
test_command	string	/testbed/run_tests.sh.
test_timeout	int	Seconds. Default 3000.
refactor_type	string	Mostly empty (legacy field from the original taxonomy).
description	string	Natural-language task prompt the model receives.
files	list[string]	Files relevant to the task.
task_type	string	feature or refactor.
categories	list[string]	Topical tags (kernel, codegen, format, etc.).

Grading

Run with pytest -v; the grading strategy parses verbose lines (preserving parametrized brackets like [False]) and uses a custom preservation rule:

success  ⇔  after.failed == 0

This handles the common case where an agent adds new tests alongside new code: if every test (original + agent-added) passes, the task is a success. Default exact-match preservation would flag the new tests as "changed" and produce false failures.

How to evaluate a model

The full evaluation harness (codebench) and the project-local benchmark glue (tensorbench) are in the supplementary code submission.

# 1. Install the harness and benchmark glue
git clone <tensorbench-repo>     ~/tensorbench
git clone <codebench-repo>       ~/codebench
pip install -e ~/codebench
pip install -r ~/tensorbench/requirements.txt

# 2. Build the eval image
cd ~/tensorbench
docker build -t scorch-eval -f dockerfiles/scorch/Dockerfile dockerfiles/scorch/

# 3. Generate predictions, then grade them
python run.py predict scorch sonnet-4.5
python run.py eval    scorch sonnet-4.5

Predictions land in predictions/; eval outputs in evaluation_results/<run_id>/.

Anonymity

This is a double-blind submission. Personally identifying information has been scrubbed from the supplementary code. Reviewers should not attempt to identify the authors via the target codebase URL or other public references.

License

MIT for the benchmark scaffolding (this dataset, Dockerfile, grading strategy). The target Scorch codebase is governed by its own upstream license.

Citation

(Anonymized for double-blind review. Citation will be added at camera-ready.)