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arXiv · 2606.05570

TensorBench: Benchmarking Coding Agents on a Compiler-Based Tensor Framework

Bobby Yan·Fredrik Kjolstad
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Abstract

Repository-level coding benchmarks face a trade-off between task difficulty and evaluation reliability: tasks that challenge frontier models often involve large codebases with incomplete test coverage, while human review does not scale. We introduce TensorBench, a benchmark of 199 feature-addition and refactoring tasks on an open-source compiler-based tensor framework that extends PyTorch with first-class support for dense and sparse tensors. Tasks cover new sparse formats, dense optimization passes, IR transformations, scheduler changes, runtime components, and high-level numerical operators. TensorBench grades each run by applying the agent's patch and running the framework's test suite, which includes the pre-existing randomized regression tests and any tests the agent adds. For feature-addition tasks, a pass means that the patched repository preserves the tested pre-existing behavior and satisfies the agent-added checks for the requested feature. We evaluate seven coding agents spanning three frontier model families and one open-weight model. Pass rates under this criterion range from $64.8\%$ for the strongest agent to $22.1\%$ for the weakest. Agents pass different subsets of tasks: pairwise Cohen's $\kappa$ ranges from $-0.07$ to $0.43$, with $\kappa = 0.05$ for the two strongest agents.

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BibTeXRIS

Bobby Yan, Fredrik Kjolstad. 2026-06-04. TensorBench: Benchmarking Coding Agents on a Compiler-Based Tensor Framework. https://arxiv.org/abs/2606.05570

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