Real issues from robotics repositories, the codebase as it was before the fix, and the developers' own tests held out. ROBO SWE measures whether an agent can resolve the bug without breaking what already works.
Share of tasks resolved, averaged over each setup's counted passes. A setup is a model plus its coding harness and reasoning setting — the whole system is what's measured.
Intervals are 95%: a 2,000-resample task bootstrap for multi-pass setups, Wilson for single-pass binary scores. Overlapping intervals mean the ordering between those setups is not settled. Different harnesses expose different tools, so compare setups, not bare model names.
Solve rate (%) for each completed setup in each part of the robotics stack. The outlined cell is the best result in that column.
Task counts per domain, from the same comparison set used for the leaderboard.
A paired comparison: the same model and harness at two reasoning-effort settings, on the same tasks.
Every task starts with a real issue and a merged fix. The agent sees the issue and the repository before the fix; the fix and the developer's tests stay hidden.
The original issue and the repository at the commit before the fix, inside its historical environment.
issue + source treeThe developer's fix and tests. One check captures the bug; seven cover behavior that already worked.
1 bug check · 7 regression checksThe reference change is 11 lines across two files — small, but it still requires understanding the issue and the system around it.
Construction checks that each issue, fix and test suite can support a reproducible evaluation before it becomes a task.
Connect the reported behavior to a merged fix and the tests that exercise it.
Prepare the repository before the fix, with its dependencies and test command.
Confirm the bug check fails before the fix and passes after it, while checking regressions.
Keep the reference solution and evaluator hidden, and record the task's provenance.
Useful evidence, with clear boundaries.
Developer tests may omit important behavior, or require details the issue doesn't fully state.
Automated before-and-after checks, AI review notes and human judgment are different evidence. The benchmark is not described as fully human-verified.
The repositories and their fixes are public. We can't rule out contamination, or turn familiarity with a repository into a claim about general capability.
Passing software tests does not establish real-world robustness, deployment readiness or robot safety.
The leaderboard is updated as evaluations finish. Each change is recorded here.
Paper and citation: TBD.
Talk to us about evaluating your setup on ROBO SWE, or about robotics coding data and environments built around the failures it surfaces.
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