foresight-swe-mini

15 of the 212 tasks in Foresight-SWE, chosen to match the full benchmark's mix of languages, difficulty and defect types while keeping runs short.

Foresight-SWE asks whether a coding agent avoids a bug nobody told it about. Each task pairs a real pull request that introduced a defect (PR-1) with the later one that fixed it (PR-2). The agent gets only PR-1's task. Resolve rate counts PR-1's tests passing. Foresight resolve rate (FRR) also requires PR-2's held-out tests to pass. See the paper and code.

Read this first. Unlike the other mini benchmarks in this collection, this subset has not been checked against real per-task results, because upstream publishes only totals and per-model breakdowns. The accuracy figures below are simulated. The tasks have not been run through the upstream harness here yet, so a task may be swapped if it turns out to be broken or very slow. Any change will be noted on this card.

What it can tell you

One task is worth 6.7 points, and at leaderboard rates most models would pass 1 to 4 of the 15. Use it to spot large differences, or to compare two setups on the same tasks. Do not use it to rank close models or to estimate a full-benchmark score.

Simulated against the 11 models on the leaderboard:

tasks	typical FRR error (points)	pairs 5+ points apart in the right order	pairs 10+ points apart in the right order
foresight-swe-mini (15)	6.8	74%	83%
random 10	8.8	64%	72%
random 15	6.9	73%	82%
random 20	6.0	78%	87%
random 30	4.7	84%	93%
random 60	3.0	94%	99%
random 100	2.0	98%	100%

The 15 do no better than a random 15 on these numbers. The published task labels explain only 4 to 13% of each model's pass or fail outcomes, so matching on them cannot do much for accuracy. What matching adds is a guaranteed mix and no very slow tasks. A subset chosen from real per-task results might do better; that needs data upstream has not released.

The simulation fits each model to Tables D.1 and D.2 of the paper, then adds a difficulty shared across models and noise per model. Those two additions are assumptions. Numbers are the middle of three runs with different shared-difficulty settings, which changed little.

The mix

	foresight-swe-mini	exact share of 15
Python / JavaScript / TypeScript / Go	8 / 3 / 2 / 2	8.1 / 3.4 / 2.0 / 1.5
PR-1 easy / medium / hard	3 / 6 / 6	3.3 / 5.4 / 6.2
PR-2 easy / medium / hard	9 / 4 / 2	8.8 / 3.7 / 2.5
Major / minor	9 / 6	9.1 / 5.9
Silent / loud	9 / 6	9.1 / 5.9
Bug / feature / mixed	8 / 6 / 1	7.9 / 6.2 / 0.8
Security	1	1.3

The 15 cover 10 of the 35 repositories, with at most 3 tasks from any one. Predicted FRR on the subset is within 0.6 points of the published score for all 11 models (data/leaderboard.csv). That only checks the mix, not real results.

How the 15 were chosen

Simulated annealing over the 158 tasks with at most 2,000 regression tests, 100 task tests, 20 held-out tests, a 60 KB patch and 3.5 GB of images. It minimises the difference from the full benchmark in the mix above, in each model's predicted scores and in task size, with a cap per repository. python build.py verified.jsonl reproduces the selection and every number here. Exact task IDs are in data/tasks.jsonl.

Running it

The tasks live upstream; this dataset only tells you which 15 to run. Build a task file:

from datasets import load_dataset
ids = set(load_dataset("LocalLLaMA/foresight-swe-mini", split="test")["instance_id"])
tasks = load_dataset("shaswat123/Foresight-SWE", split="test")
tasks.filter(lambda r: r["instance_id"] in ids).to_json("foresight_mini.jsonl")

Then set DATASET to that file in one of the upstream eval scripts. Each task needs one amd64 Docker image, about 11 GB in total. To compare with the leaderboard, keep the paper's budgets: 250 to 350 steps and $3 per task in mini-swe-agent.

Report resolve rate and FRR, and label results "foresight-swe-mini". Scores on the 15 are not Foresight-SWE scores.

Credit

All tasks and the benchmark design are by Patel, Liao, Henderson, He and Chen.

@misc{patel2026codingagentscatchbugs,
      title={Do Coding Agents Catch Their Own Bugs? (They Don't.)},
      author={Shaswat Patel and Michel Liao and Peter Henderson and He He and Valerie Chen},
      year={2026},
      eprint={2611.01181},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2611.01181},
}