foresight-swe-mini: 15 of the 212 Foresight-SWE tasks

README.md

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+---
+license: mit
+language: [en]
+pretty_name: foresight-swe-mini
+tags: [benchmark, coding-agents, software-engineering, subset, foresight-swe]
+size_categories: [n<1K]
+configs:
+  - config_name: default
+    data_files:
+      - split: test
+        path: data/tasks.jsonl
+---
+
+# foresight-swe-mini
+
+15 of the 212 tasks in [Foresight-SWE](https://huggingface.co/datasets/shaswat123/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](https://arxiv.org/abs/2611.01181) and [code](https://github.com/shaswatpatel123/Foresight-SWE).
+
+**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:
+
+```python
+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](https://github.com/shaswatpatel123/Foresight-SWE/tree/main/eval). 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.
+
+```bibtex
+@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},
+}
+```

build.py

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+"""Select foresight-swe-mini and reproduce every number on the card.
+
+    python build.py verified.jsonl
+
+verified.jsonl is data/verified.jsonl from shaswat123/Foresight-SWE. Writes
+data/tasks.jsonl, data/leaderboard.csv and data/metrics.json. Upstream publishes
+totals and per-model breakdowns only, so accuracy is simulated, not measured.
+"""
+import collections as C, csv, json, math, sys
+import numpy as np
+from scipy.stats import spearmanr
+
+rows = [json.loads(l) for l in open(sys.argv[1])]
+sizes = json.load(open("data/image_sizes.json"))
+L = lambda v: json.loads(v) if isinstance(v, str) else (v or [])
+N, K = len(rows), 15
+col = lambda k: np.array([r[k] for r in rows])
+ids, repo, lang, d1, d2, fm, tt, sec = (col(k) for k in
+    ("instance_id", "repo", "language", "pr1_difficulty", "difficulty", "failure_mode", "pr1_task_type", "security"))
+sev = np.array(["minor" if r["severity"] == "minor" else "major" for r in rows])
+count = lambda k: np.array([len(L(r[k])) for r in rows])
+p2p, f2p, ant = count("PASS_TO_PASS"), count("FAIL_TO_PASS"), count("ANTICIPATION_FAIL_TO_PASS")
+patch_kb = np.array([len(r["patch"]) / 1e3 for r in rows])
+tag = lambda r, k: sizes[r[k].split(":")[1]] / 1e9
+image_gb = np.array([tag(r, "docker_image") for r in rows])
+both_images_gb = image_gb + np.array([tag(r, "pr2base_image") for r in rows])
+
+# Paper, Tables D.1 and D.2, in points:
+# PR-1 easy/medium/hard, PR-2 easy/medium/hard, minor/major, loud/silent.
+MODELS = ["Qwen3.8 Flash Next", "GLM-5.3 Flash", "DeepSeek-V4 Flash", "GPT-5.6 Sol", "GPT-5.6 Terra",
+          "GPT-5.6 Luna", "Claude Opus 5", "Claude Sonnet 5", "Qwen3.5-397B-A17B", "Qwen3.8-27B", "Qwen3.5-27B"]
+RESOLVE = np.array([
+    [95.7, 97.4, 85.2, 91.2, 98.1, 85.7, 91.6, 92.2, 92.8, 91.5], [76.6, 80.5, 83.0, 73.6, 90.4, 91.4, 79.5, 81.4, 80.7, 80.6],
+    [68.1, 59.7, 52.3, 55.2, 69.2, 54.3, 59.0, 58.1, 61.4, 56.6], [93.6, 81.8, 81.8, 80.0, 90.4, 91.4, 88.0, 82.2, 90.4, 80.6],
+    [85.1, 79.2, 64.8, 76.8, 71.2, 71.4, 77.1, 72.9, 83.1, 69.0], [87.2, 76.6, 64.8, 72.0, 76.9, 77.1, 74.7, 73.6, 78.3, 71.3],
+    [85.1, 74.0, 77.3, 74.4, 88.5, 74.3, 77.1, 78.3, 78.3, 77.5], [76.6, 57.1, 53.4, 56.8, 65.4, 62.9, 62.7, 58.1, 65.1, 56.6],
+    [46.8, 23.4, 6.8, 22.4, 23.1, 17.1, 24.1, 20.2, 24.1, 20.2], [53.2, 27.3, 9.1, 25.6, 28.8, 20.0, 25.3, 25.6, 26.5, 24.8],
+    [38.3, 23.4, 3.4, 17.6, 25.0, 11.4, 18.1, 18.6, 16.9, 19.4]]) / 100
+FRR = np.array([
+    [27.7, 31.2, 8.0, 28.8, 15.4, 0.0, 20.5, 20.9, 25.3, 17.8], [17.0, 14.3, 5.7, 12.0, 15.4, 2.9, 13.3, 10.1, 15.7, 8.5],
+    [21.3, 29.9, 8.0, 21.6, 23.1, 2.9, 24.1, 15.5, 24.1, 15.5], [21.3, 23.4, 12.5, 24.8, 15.4, 0.0, 18.1, 18.6, 20.5, 17.1],
+    [25.5, 16.9, 8.0, 20.8, 9.6, 2.9, 18.1, 13.2, 18.1, 13.2], [23.4, 23.4, 6.8, 20.8, 17.3, 0.0, 18.1, 15.5, 20.5, 14.0],
+    [38.3, 26.0, 15.9, 28.0, 30.8, 2.9, 33.7, 18.6, 32.5, 19.4], [21.3, 20.8, 11.4, 19.2, 19.2, 5.7, 22.9, 13.2, 21.7, 14.0],
+    [10.6, 11.7, 1.1, 8.8, 5.8, 2.9, 10.8, 4.7, 9.6, 5.4], [12.8, 10.4, 2.3, 8.8, 7.7, 2.9, 9.6, 6.2, 10.8, 5.4],
+    [10.6, 9.1, 1.1, 7.2, 7.7, 0.0, 7.2, 5.4, 4.8, 7.0]]) / 100
+FULL_FRR = np.array([20.75, 11.32, 18.87, 18.40, 15.09, 16.51, 24.53, 16.98, 7.08, 7.55, 6.13])
+FULL_RESOLVE = np.array([91.98, 80.66, 58.49, 84.43, 74.53, 74.06, 77.83, 59.91, 21.70, 25.47, 18.40])
+
+X = np.array([d1 == "easy", d1 == "medium", d1 == "hard", d2 == "easy", d2 == "medium", d2 == "hard",
+              sev == "minor", sev == "major", fm == "loud", fm == "silent"], float).T
+XX = np.c_[np.ones(N), X[:, [0, 1, 3, 4, 6, 8]]]
+sig = lambda z: 1 / (1 + np.exp(-z))
+
+
+def fit(margin):
+    """Additive logit whose per-group means match one model's table row."""
+    counts = margin * X.sum(0)
+    t = np.r_[counts[:3].sum(), counts[[0, 1, 3, 4, 6, 8]]]
+    b = np.zeros(XX.shape[1])
+    b[0] = math.log((t[0] / N + 1e-3) / (1 - t[0] / N + 1e-3))
+    for _ in range(200):
+        p = sig(XX @ b)
+        H = (XX * (p * (1 - p))[:, None]).T @ XX + 1e-3 * np.eye(len(b))
+        b += 0.5 * np.linalg.solve(H, t - XX.T @ p)
+    return b
+
+
+bF = np.array([fit(m) for m in FRR])
+pF, pR = sig(XX @ bF.T), sig(XX @ np.array([fit(m) for m in RESOLVE]).T)
+
+feats = np.c_[X, *[(lang == l) * 1. for l in ("python", "javascript", "typescript", "go")],
+              (tt == "feature") * 1., (tt == "bug") * 1., sec * 1.,
+              (repo == "sveltejs/svelte") * 1., (repo == "django/django") * 1.]
+Z = np.c_[np.log10(p2p + 1), np.log10(patch_kb + 1), np.log10(ant + 1)]
+
+
+def objective(S):
+    return (((feats[S].mean(0) - feats.mean(0)) ** 2).sum()
+            + ((pF[S].mean(0) - pF.mean(0)) ** 2).sum() * 8 + ((pR[S].mean(0) - pR.mean(0)) ** 2).sum() * 2
+            + (((Z[S].mean(0) - Z.mean(0)) / Z.std(0)) ** 2).sum() * .02)
+
+
+def search(pool, seed, iters=25000):
+    r = np.random.default_rng(seed)
+    S = list(r.choice(pool, K, replace=False))
+    over = lambda s: any(v > max(1, math.ceil((repo == rp).mean() * K)) for rp, v in C.Counter(repo[s]).items())
+    v, T = objective(S) + over(S), .002
+    for _ in range(iters):
+        i, o = r.integers(K), r.choice(pool)
+        if o in S:
+            continue
+        S2 = S.copy()
+        S2[i] = o
+        v2 = objective(S2) + over(S2)
+        if v2 < v or r.random() < math.exp(-(v2 - v) / max(T, 1e-9)):
+            S, v = S2, v2
+        T *= .9995
+    return sorted(S), v
+
+
+# Keep runs short: leave out the tasks that are slow to grade.
+pool = np.where((p2p <= 2000) & (ant <= 20) & (f2p <= 100) & (patch_kb <= 60) & (both_images_gb <= 3.5))[0]
+S = min((search(pool, s) for s in range(8)), key=lambda x: x[1])[0]
+
+
+def calibrate(lin, target):
+    a, b = -8., 8.
+    for _ in range(40):
+        c = (a + b) / 2
+        a, b = (c, b) if sig(lin + c).mean() < target else (a, c)
+    return c
+
+
+def simulate(sd, reps=500, seed=7):
+    """Shared per-task difficulty (sd, logits) plus per-model noise, on top of the fit."""
+    rng, out = np.random.default_rng(seed), C.defaultdict(list)
+    for _ in range(reps):
+        lin = XX @ bF.T + rng.normal(0, sd, N)[:, None] + rng.normal(0, .5, (N, 11))
+        for m in range(11):
+            lin[:, m] += calibrate(lin[:, m], FULL_FRR[m] / 100)
+        Y = (rng.random((N, 11)) < sig(lin)) * 1.
+        full = Y.mean(0)
+        designs = {f"random {k}": rng.choice(N, k, replace=False) for k in (10, 15, 20, 30, 60, 100)}
+        designs["foresight-swe-mini"] = np.array(S)
+        for name, D in designs.items():
+            e = Y[D].mean(0)
+            out[name, "error_points"].append(np.abs(e - full).mean() * 100)
+            for gap in (.05, .10):
+                pairs = [(a, b) for a in range(11) for b in range(a + 1, 11) if abs(full[a] - full[b]) >= gap]
+                out[name, f"pairs_{int(gap * 100)}pt"].append(
+                    sum((e[a] - e[b]) * (full[a] - full[b]) > 0 for a, b in pairs) / max(len(pairs), 1) * 100)
+    table = C.defaultdict(dict)
+    for (name, metric), v in out.items():
+        table[name][metric] = round(float(np.mean(v)), 1)
+    return table
+
+
+keep = ("instance_id", "repo", "language", "earlier_pr", "later_pr", "pr1_difficulty", "difficulty", "severity",
+        "failure_mode", "pr1_task_type", "security", "docker_image")
+with open("data/tasks.jsonl", "w") as f:
+    for i in S:
+        row = {k: rows[i][k] for k in keep}
+        row.update(n_fail_to_pass=int(f2p[i]), n_anticipation=int(ant[i]), n_pass_to_pass=int(p2p[i]),
+                   image_gb=round(float(image_gb[i]), 2))
+        f.write(json.dumps(row) + "\n")
+with open("data/leaderboard.csv", "w", newline="") as f:
+    w = csv.writer(f)
+    w.writerow(["model", "resolve_all_212", "frr_all_212", "predicted_frr_on_mini"])
+    for m, name in enumerate(MODELS):
+        w.writerow([name, FULL_RESOLVE[m], FULL_FRR[m], round(float(pF[S].mean(0)[m] * 100), 1)])
+mix = dict(language=lang, pr1_difficulty=d1, pr2_difficulty=d2, severity=sev, detectability=fm, task_type=tt, security=sec)
+metrics = dict(
+    n_pool=int(len(pool)), image_gb=round(float(image_gb[S].sum()), 1), max_pass_to_pass=int(p2p[S].max()),
+    repos=len(set(repo[S])),
+    predicted_frr_max_error=round(float(np.abs(pF[S].mean(0) - pF.mean(0)).max() * 100), 1),
+    predicted_resolve_max_error=round(float(np.abs(pR[S].mean(0) - pR.mean(0)).max() * 100), 1),
+    variance_explained_frr=[round(float(x), 3) for x in pF.var(0) / (pF.mean(0) * (1 - pF.mean(0)))],
+    spearman_resolve_frr_all=round(float(spearmanr(FULL_RESOLVE, FULL_FRR)[0]), 2),
+    mix={k: dict(mini=dict(C.Counter(str(x) for x in v[S])),
+                 proportional={str(a): round(b / N * K, 1) for a, b in C.Counter(v.tolist()).items()})
+         for k, v in mix.items()},
+    simulation={str(sd): simulate(sd) for sd in (.5, 1, 1.5)})
+json.dump(metrics, open("data/metrics.json", "w"), indent=1)
+print(" ".join(str(ids[i]) for i in S))

data/image_sizes.json

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+{"inst-prometheus__9324-9421-pr2base": 1439259427, "inst-prometheus__9324-9421-pr1": 1439589842, "inst-prometheus__18206-18845-pr1": 1737031282, "inst-prometheus__18206-18845-pr2base": 1761190226, "inst-prometheus__17707-17828-pr2base": 1579536053, "inst-prometheus__17707-17828-pr1": 1579672362, "inst-prometheus__17631-17731-pr2base": 1556441854, "inst-prometheus__17631-17731-pr1": 1556372449, "inst-prometheus__17469-17863-pr2base": 1579616246, "inst-prometheus__17469-17863-pr1": 1579537843, "inst-prometheus__16457-17353-pr2base": 1549115775, "inst-prometheus__16457-17353-pr1": 1518955420, "inst-prometheus__16249-16713-pr2base": 1487132281, "inst-prometheus__16249-16478-pr2base": 1487037162, "inst-prometheus__16249-16713-pr1": 1484270498, "inst-prometheus__16249-16478-pr1": 1484245278, "inst-prometheus__14769-16340-pr2base": 1449095009, "inst-prometheus__14769-16340-pr1": 1343833407, "inst-prometheus__13583-14446-pr2base": 1359650240, "inst-prometheus__13583-14446-pr1": 1400347298, "inst-prometheus__13001-13190-pr2base": 1377474720, "inst-prometheus__12350-12554-pr1": 1379059587, "inst-prometheus__13001-13190-pr1": 1376973618, "inst-prometheus__12350-12554-pr2base": 1430327095, "inst-prometheus__10490-14654-pr1": 1359888661, "inst-helm__31176-35205-pr2base": 1109338514, "inst-helm__31176-35205-pr1": 1011568869, "inst-prometheus__10490-14654-pr2base": 1342182773, "inst-gitea__38039-38170-pr2base": 2737025414, "inst-gitea__38039-38170-pr1": 2734830415, "inst-gin__4295-4373-pr2base": 448200207, "inst-gin__4295-4373-pr1": 449254808, "inst-caddy__7265-7463-pr2base": 1149649958, "inst-caddy__7265-7463-pr1": 1057646780, "inst-argocd__28815-29350-pr1": 2228050048, "inst-argocd__28815-29350-pr2base": 2228186872, "inst-pandas__56650-58052-pr2base": 834114634, "inst-pandas__56650-58052-pr1": 856087104, "inst-pandas__56290-57388-pr2base": 847142260, "inst-pandas__56290-57388-pr1": 809942769, "inst-pandas__51307-51674-pr2base": 864838694, "inst-pandas__51307-51674-pr1": 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data/leaderboard.csv

@@ -0,0 +1,12 @@
+model,resolve_all_212,frr_all_212,predicted_frr_on_mini
+Qwen3.8 Flash Next,91.98,20.75,21.2
+GLM-5.3 Flash,80.66,11.32,11.3
+DeepSeek-V4 Flash,58.49,18.87,19.4
+GPT-5.6 Sol,84.43,18.4,19.0
+GPT-5.6 Terra,74.53,15.09,15.2
+GPT-5.6 Luna,74.06,16.51,16.9
+Claude Opus 5,77.83,24.53,24.8
+Claude Sonnet 5,59.91,16.98,17.2
+Qwen3.5-397B-A17B,21.7,7.08,6.7
+Qwen3.8-27B,25.47,7.55,7.4
+Qwen3.5-27B,18.4,6.13,6.0

data/metrics.json

@@ -0,0 +1,217 @@
+{
+ "n_pool": 158,
+ "image_gb": 11.0,
+ "max_pass_to_pass": 1872,
+ "repos": 10,
+ "predicted_frr_max_error": 0.6,
+ "predicted_resolve_max_error": 0.9,
+ "variance_explained_frr": [
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+  0.046,
+  0.083,
+  0.063,
+  0.064,
+  0.074,
+  0.09,
+  0.036,
+  0.061,
+  0.04,
+  0.06
+ ],
+ "spearman_resolve_frr_all": 0.67,
+ "mix": {
+  "language": {
+   "mini": {
+    "python": 8,
+    "go": 2,
+    "javascript": 3,
+    "typescript": 2
+   },
+   "proportional": {
+    "python": 8.1,
+    "typescript": 2.0,
+    "javascript": 3.4,
+    "go": 1.5
+   }
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+  "pr1_difficulty": {
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+    "hard": 6,
+    "easy": 3
+   },
+   "proportional": {
+    "hard": 6.2,
+    "easy": 3.3,
+    "medium": 5.4
+   }
+  },
+  "pr2_difficulty": {
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+    "easy": 9,
+    "hard": 2
+   },
+   "proportional": {
+    "medium": 3.7,
+    "easy": 8.8,
+    "hard": 2.5
+   }
+  },
+  "severity": {
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+    "major": 9,
+    "minor": 6
+   },
+   "proportional": {
+    "major": 9.1,
+    "minor": 5.9
+   }
+  },
+  "detectability": {
+   "mini": {
+    "silent": 9,
+    "loud": 6
+   },
+   "proportional": {
+    "silent": 9.1,
+    "loud": 5.9
+   }
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+  "task_type": {
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+    "bug": 8,
+    "mixed": 1
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+   "proportional": {
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+    "mixed": 0.8
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+  "security": {
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+    "False": 14
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+   "proportional": {
+    "False": 13.7,
+    "True": 1.3
+   }
+  }
+ },
+ "simulation": {
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+    "pairs_5pt": 63.6,
+    "pairs_10pt": 72.0
+   },
+   "random 15": {
+    "error_points": 6.8,
+    "pairs_5pt": 72.4,
+    "pairs_10pt": 81.6
+   },
+   "random 20": {
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+    "pairs_5pt": 77.6,
+    "pairs_10pt": 86.7
+   },
+   "random 30": {
+    "error_points": 4.7,
+    "pairs_5pt": 83.8,
+    "pairs_10pt": 92.8
+   },
+   "random 60": {
+    "error_points": 3.0,
+    "pairs_5pt": 93.8,
+    "pairs_10pt": 98.6
+   },
+   "random 100": {
+    "error_points": 2.0,
+    "pairs_5pt": 98.1,
+    "pairs_10pt": 99.9
+   },
+   "foresight-swe-mini": {
+    "error_points": 6.7,
+    "pairs_5pt": 73.8,
+    "pairs_10pt": 83.6
+   }
+  },
+  "1": {
+   "random 10": {
+    "error_points": 8.8,
+    "pairs_5pt": 63.8,
+    "pairs_10pt": 72.5
+   },
+   "random 15": {
+    "error_points": 6.9,
+    "pairs_5pt": 73.0,
+    "pairs_10pt": 82.3
+   },
+   "random 20": {
+    "error_points": 6.0,
+    "pairs_5pt": 78.1,
+    "pairs_10pt": 87.1
+   },
+   "random 30": {
+    "error_points": 4.7,
+    "pairs_5pt": 84.1,
+    "pairs_10pt": 92.6
+   },
+   "random 60": {
+    "error_points": 3.0,
+    "pairs_5pt": 94.3,
+    "pairs_10pt": 99.0
+   },
+   "random 100": {
+    "error_points": 2.0,
+    "pairs_5pt": 98.3,
+    "pairs_10pt": 100.0
+   },
+   "foresight-swe-mini": {
+    "error_points": 6.8,
+    "pairs_5pt": 74.3,
+    "pairs_10pt": 83.3
+   }
+  },
+  "1.5": {
+   "random 10": {
+    "error_points": 8.7,
+    "pairs_5pt": 63.6,
+    "pairs_10pt": 72.3
+   },
+   "random 15": {
+    "error_points": 6.9,
+    "pairs_5pt": 73.8,
+    "pairs_10pt": 83.2
+   },
+   "random 20": {
+    "error_points": 6.0,
+    "pairs_5pt": 79.1,
+    "pairs_10pt": 88.2
+   },
+   "random 30": {
+    "error_points": 4.7,
+    "pairs_5pt": 85.3,
+    "pairs_10pt": 93.4
+   },
+   "random 60": {
+    "error_points": 3.0,
+    "pairs_5pt": 94.8,
+    "pairs_10pt": 99.3
+   },
+   "random 100": {
+    "error_points": 2.0,
+    "pairs_5pt": 98.6,
+    "pairs_10pt": 100.0
+   },
+   "foresight-swe-mini": {
+    "error_points": 6.8,
+    "pairs_5pt": 74.0,
+    "pairs_10pt": 83.3
+   }
+  }
+ }
+}

data/tasks.jsonl

@@ -0,0 +1,15 @@
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+{"instance_id": "django__21204-34570", "repo": "django/django", "language": "python", "earlier_pr": 21204, "later_pr": 34570, "pr1_difficulty": "hard", "difficulty": "easy", "severity": "minor", "failure_mode": "loud", "pr1_task_type": "bug", "security": false, "docker_image": "docker.io/shaswatpatel123/foresight-swe:inst-django__21204-34570-pr1", "n_fail_to_pass": 3, "n_anticipation": 1, "n_pass_to_pass": 312, "image_gb": 0.61}
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+{"instance_id": "prometheus__14513-14598", "repo": "prometheus/prometheus", "language": "go", "earlier_pr": 14513, "later_pr": 14598, "pr1_difficulty": "hard", "difficulty": "hard", "severity": "minor", "failure_mode": "loud", "pr1_task_type": "bug", "security": false, "docker_image": "docker.io/shaswatpatel123/foresight-swe:inst-prometheus__14513-14598-pr1", "n_fail_to_pass": 2, "n_anticipation": 1, "n_pass_to_pass": 269, "image_gb": 1.34}
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+{"instance_id": "prometheus__13583-14446", "repo": "prometheus/prometheus", "language": "go", "earlier_pr": 13583, "later_pr": 14446, "pr1_difficulty": "medium", "difficulty": "medium", "severity": "critical", "failure_mode": "silent", "pr1_task_type": "bug", "security": false, "docker_image": "docker.io/shaswatpatel123/foresight-swe:inst-prometheus__13583-14446-pr1", "n_fail_to_pass": 4, "n_anticipation": 4, "n_pass_to_pass": 93, "image_gb": 1.4}