EvalHarness/evalharness/data/datasets/swe_bench_verified_agentic.py
sora f662006517 swe_bench_verified_agentic: multi-turn SWE agent (mini-swe-agent protocol)
Ports es's swe_bench_agentic_adapter into our plugin architecture:
- env swe_agentic: per-sample LONG-RUNNING container (official sweb
  image, /testbed, bash -lc like the testbed startup files expect),
  single bash tool via function calling, sentinel-submission protocol
  (COMPLETE_TASK_AND_SUBMIT_FINAL_OUTPUT + patch), git-diff fallback;
  observations capped at 30k chars
- dataset swe_bench_verified_agentic: same princeton source/converter,
  separate bench name so both variants coexist
- recipe: recovered patch + OFFICIAL test_patch applied in-container,
  FAIL_TO_PASS + capped PASS_TO_PASS via conda testbed pytest, 1800s
- config: max_turns 250, env swe_agentic

Single-turn swe_bench_verified is untouched.

Co-Authored-By: Claude <noreply@anthropic.com>
2026-09-18 02:29:35 +00:00

27 lines
874 B
Python

"""swe_bench_verified_agentic: same data as swe_bench_verified, agentic bench.
Separate bench name so both variants coexist (single-turn oracle vs
multi-turn agent); source/split/fields identical — the difference lives
in the recipe (env loop + official harness scoring) and config."""
from ..registry import register_dataset
from ..spec import DatasetSpec
@register_dataset(
DatasetSpec(
name='swe_bench_verified_agentic',
source='princeton-nlp/SWE-bench_Verified',
split='test',
task_type='agent',
tags=['code', 'agent', 'swe'],
requires=['docker'],
description='SWE-bench Verified (agentic): multi-turn bash agent in '
'the per-instance /testbed container.',
)
)
def swe_bench_verified_agentic():
from .swe_bench_verified import _record_to_sample
return _record_to_sample