Datasets:

CLoud5-real
/

longrca-bench

pretty_name: LongRCA Bench
language:
  - en
size_categories:
  - 1K<n<10K
annotations_creators:
  - expert-generated
tags:
  - agents
  - multi-agent-systems
  - failure-analysis
  - root-cause-analysis
configs:
  - config_name: default
    default: true
    data_files:
      - split: test
        path: data/test-*.parquet

LongRCA Bench

LongRCA Bench contains 1,140 observed, non-injected failed agent trajectories from five task domains. Each trajectory has human annotations for the responsible role, earliest decisive root-cause step, and a trajectory-grounded rationale.

For the benchmark definition, annotation protocol, and evaluation results, see:

LongRCA Bench: Diagnosing Responsible Roles and Root Causes in Long-Horizon Agent Failures

Dataset Composition

Source	Instances
SWE-bench Pro	128
Terminal Bench 2	42
TravelPlanner	685
VitaBench	108
WebArena Verified	177
Total	1,140

The dataset contains 178,137 recorded history steps. The median trajectory length is 145 steps, and the maximum is 728 steps.

Data Fields

Field	Description
`question_ID`	Formal source-prefixed trajectory identifier (`<source>__NNN`)
`history`	Complete ordered trajectory
`mistake_agent`	Reference responsible role
`mistake_step`	Reference 0-based root-cause step
`mistake_reason`	Human-written rationale; not scored

The responsible role and root-cause step are evaluated independently. The role must not be inferred automatically from the emitter of mistake_step. Release IDs are numbered independently within each source and should be treated as stable, opaque keys.

Usage

from datasets import load_dataset

dataset = load_dataset(
    "CLoud5-real/longrca-bench",
    split="test",
)

Source Composition

Source benchmark	Task domain	Trajectories
SWE-bench Pro	Software repair	128
Terminal Bench 2	Terminal tasks	42
TravelPlanner	Travel planning	685
VitaBench	Service-oriented tool use	108
WebArena Verified	Web interaction	177
Total	Five task domains	1,140

The trajectories were generated with MiniMax-M2.5, Kimi-K2.5, and Qwen3.5-Plus under several agent organizations, including fixed-role teams, specialist group chats, sequential workflows, and planner–critic–executor workflows.

Citation

If you use LongRCA Bench, please cite:

@misc{zhang2026longrcabench,
  title        = {LongRCA Bench: Diagnosing Responsible Roles and Root Causes
                  in Long-Horizon Agent Failures},
  author       = {Yunfei Zhang and Boyu Feng and Changhua Pei and
                  Zexin Wang and Zhihuang Peng and Xinlong Liu and
                  Hengyue Jiang and Difeng Ma and Jiayi Zhang and
                  Yongzhou Yao and Yanan Zhao and Fei Sun and
                  Yintong Huo and Zhaoyang Liu and Jingjing Li and
                  Gaogang Xie and Dan Pei},
  year         = {2026},
  eprint       = {2608.15242},
  archivePrefix = {arXiv},
  primaryClass = {cs.AI},
  url          = {https://arxiv.org/abs/2608.15242}
}