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IDMedicine/code-graph-trajeval-v1
code-graph-trajeval-v1 is a machine learning model from IDMedicine. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Companion artifact for the trajectory-probe downstream eval of the code-graph-v7 encoders (W1, I6, ...).
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Updated Apr 24, 2026
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From the Hugging Face model README
Companion artifact for the trajectory-probe downstream eval of the code-graph-v7 encoders (W1, I6, ...).
traj_full_bundle.tar.gz (488 MB) — contains:
specs.jsonl: 2456 SWE-Bench Verified agent trajectories harvested
from swe-bench-submissions S3 bucket. Fields: instance_id, traj_id,
repo, base_commit, patches (1 entry = final model patch), resolved.repos/: shallow (--filter=blob:none) clones of the 12 target
repos (django, sympy, sphinx, matplotlib, scikit-learn, astropy,
xarray, pytest, pylint, requests, seaborn, flask). ~671 MB
uncompressed. Blobs pulled lazily per base_commit checkout.graphjepa/: pipeline code (trajectory_pipeline, trajectory_realize,
trajectory_probe, trajectory_harvest) plus scripts/trajectory_full.sh.harvest.log — stdout from the S3 harvester that produced specs.jsonl.tar -xzf traj_full_bundle.tar.gz
rsync -a traj_full/graphjepa/ graphjepa/
mkdir -p outputs/traj_real
cp traj_full/specs.jsonl outputs/traj_real/
mv traj_full/repos outputs/traj_real/repos
# realize (4 sharded workers by repo)
SHARDS=4 bash graphjepa/scripts/trajectory_full.sh
tail -f outputs/traj_real/logs/realize_shard*.log
# merge manifests + probe with each encoder
cat outputs/traj_real/manifest_shard*.jsonl > outputs/traj_real/manifest.jsonl
for NAME in W1_softplus_s0 I6_joint_s0; do
.venv/bin/python -m graphjepa.trajectory_probe \
--manifest outputs/traj_real/manifest.jsonl \
--ckpt outputs/$NAME/ckpt_final.pt \
--pool mean --split-by repo \
--output outputs/traj_real/probe_${NAME}.json
done
Specs harvested from 5 SWE-Bench Verified submissions:
| Submission | N | Resolved | Rate |
|---|---|---|---|
| 20240620_sweagent_claude3.5sonnet | 485 | 168 | 34.6% |
| 20241022_tools_claude-3-5-sonnet-updated | 483 | 245 | 50.7% |
| 20241028_agentless-1.5_gpt4o | 495 | 194 | 39.2% |
| 20241029_OpenHands-CodeAct-2.1-sonnet | 493 | 265 | 53.8% |
| 20250405_amazon-q-developer-2025 | 500 | 330 | 66.0% |
| total | 2456 | 1202 | 48.9% |
500 unique instance_ids, 499 unique base_commits (median 5 trajectories per commit — different agents attempting the same task).