CORRECT

CORRECT: Condensed Error Recognition via Knowledge Transfer in Multi-agent Systems

CORRECT is a training-free framework that distills past multi-agent-system (MAS) failures into compact, reusable error schemata and reuses them at inference time to localize decisive errors in new failure trajectories. This repository also releases CORRECT-Error, a 2,226-trajectory benchmark for step-level error attribution across seven MAS tasks.

The CORRECT-Error benchmark

CORRECT-Error is a benchmark for step-level error attribution in multi-agent systems. Each example is a complete multi-agent failure trajectory — the full agent-by-agent interaction produced by Magentic-One on a standard task — annotated with the single decisive error that caused the failure: which agent made it (mistake_agent), at which step (mistake_step), and why (mistake_reason), together with the task question and groundtruth.

It spans seven tasks (GAIA, HotpotQA, Musique, WikiMQA, ARC, Math500, MMLU-Pro) and two generator models, for 2,226 labeled trajectories in total:

Generator model	Trajectories
GPT-4o-mini	318
GPT-5-Nano	1,908
Total	2,226

The task is to read a trajectory and localize its decisive error (the agent and step). See data/README.md for the per-record schema; the dataset is on Hugging Face at https://huggingface.co/datasets/yifanyu/CORRECT-Error.

Repository structure

src/        inference and schema/similarity generation code
data/       error schemata and trajectory-similarity mappings
scripts/    shell entry point for each step

The error schemata (data/schemata_*) and similarity mappings (data/similarities_*) used in the paper are included in the repository. Only the trajectory data is downloaded separately, as shown below.

Installation

pip install -r requirements.txt

CORRECT-Error (Table 2)

bash scripts/download_data.sh                # download trajectories from Hugging Face
bash scripts/run_inference_correct_error.sh  # GPT-5-Nano split, k=5 (paper §A.3)

SPLIT=gpt4omini runs the GPT-4o-mini split.

Who&When (Table 1)

bash scripts/download_whoandwhen.sh          # clone the upstream Who&When data
bash scripts/run_inference_whoandwhen.sh     # Qwen-2.5-7B detector

Select a cloud detector with the MODEL variable:

MODEL=gpt-5 OPENAI_API_KEY=sk-... bash scripts/run_inference_whoandwhen.sh
MODEL=gemini-2.5-flash GOOGLE_APPLICATION_CREDENTIALS=key.json \
  GOOGLE_CLOUD_PROJECT=my-project bash scripts/run_inference_whoandwhen.sh

Each script documents its options in a header comment.

Scoring

Both runners write per-prediction text files (Prediction for <id>.json: ...). Score them with src/evaluate.py, the strict exact-match evaluator used in the paper. The reported metric is step-level accuracy; Step Accuracy (tolerance 0) is the exact-match number the paper reports, so pass --tolerance 0.

# CORRECT-Error (Table 2): ground truth = the downloaded trajectories
python src/evaluate.py --tolerance 0 \
  --eval_file outputs/all_at_once_similarity_5schemata_qwen-7b_gpt5nano_musique.txt \
  --data_path data/correct_error_gpt5nano/musique/individual_trajectories

# Who&When (Table 1): ground truth = the downloaded subset
python src/evaluate.py --tolerance 0 \
  --eval_file outputs_whoandwhen/all_at_once_10schemata_qwen-7b_hand_crafted.txt \
  --data_path data/whoandwhen/Hand-Crafted

Regenerating schemata and similarities

The included artifacts cover the paper setup. Regenerate them only to use a different model or a modified trajectory set.

Task	Script	Module
Error schemata (local vLLM)	`scripts/generate_schemata.sh`	`src/error_schema_generator.py`
Error schemata (OpenAI / Gemini)	`scripts/generate_schemata_cloud.sh`	`src/error_schema_generator_cloud.py`
Similarities — CORRECT-Error gpt-4o-mini	`scripts/generate_similarities.sh`	`src/generate_trajectory_similarities.py`
Similarities — CORRECT-Error gpt-5-nano	`scripts/generate_similarities_gpt5nano.sh`	`src/generate_trajectory_similarities.py`
Similarities — Who&When	(run module directly)	`src/generate_trajectory_similarities.py`

Loading the dataset

from datasets import load_dataset

ds = load_dataset("yifanyu/CORRECT-Error", split="test")
row = ds[0]
print(row["dataset"], row["generator_model"], row["question_id"])
print("Decisive error at step", row["mistake_step"], "by", row["mistake_agent"])

The benchmark is hosted at https://huggingface.co/datasets/yifanyu/CORRECT-Error. See data/README.md for the record schema and the layout of the schemata and similarity files.

Citation

@inproceedings{yu2026correct,
  title     = {{CORRECT}: Condensed Error Recognition via Knowledge Transfer in Multi-agent Systems},
  author    = {Yu, Yifan and Li, Moyan and Xu, Shaoyuan and Fu, Jinmiao and
               Hou, Xinhai and Lai, Fan and Wang, Bryan},
  booktitle = {Proceedings of the 43rd International Conference on Machine Learning},
  year      = {2026}
}

Acknowledgements

Inference and schema-generation code is adapted from Agents_Failure_Attribution (Zhang et al., ICML 2025). The CORRECT-Error trajectories were generated with Magentic-One (Fourney et al., 2024). See NOTICE for full attribution.

License

Apache 2.0. See LICENSE.