---
license: mit
task_categories:
  - text-generation
language:
  - en
tags:
  - benchmark
  - code
  - llm-evaluation
  - feature-addition
  - python
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: test
        path: tensorbench.json
---

# TensorBench

Feature-addition benchmark for LLMs and coding agents, evaluated against the
[Scorch](https://github.com/bobbyyyan/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`](https://github.com/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 --hard`s 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.

```bash
# 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.)