README.md

metadata

license: mit
language:
  - en
pretty_name: DistDebug-Bench Traces
size_categories:
  - 1K<n<10K
tags:
  - distributed-systems
  - debugging
  - root-cause-analysis
  - agents
  - trajectories
  - software-engineering
configs:
  - config_name: attempts
    data_files:
      - split: train
        path: data/attempts.parquet
    default: true

DistDebug-Bench Traces

Agent traces behind the main results (Table 1) of DistDebug-Bench: Can LLM Agents Diagnose Bugs in Distributed Systems? (NeurIPS 2026, Evaluations and Datasets Track): 17 agent configurations, 120 bugs each, and the three attempts per bug that the paper scores, for 6,120 attempts in total.

• Benchmark: https://huggingface.co/datasets/OrderLab/distdebug-bench
• Code, runner, and evaluator: https://github.com/OrderLab/distdebug-bench
• Website and leaderboard: https://distdebug.com

Contents

Path	Description
data/attempts.parquet	One row per attempt: configuration, bug, run, the nine accuracy scores, token use and cost, and the diagnosis.
runs/<config>.tar.gz	Every attempt's full run directory, including the trace.
configs.json	Harness, harness version, and model of each configuration.
selection.json	The run IDs of the three scored attempts per bug and configuration.

Configuration	Harness	Model	Archive
mini-swe-agent__gpt-6-astra	Mini-SWE-Agent 2.2.8	GPT-6 Astra	14 MB
mini-swe-agent__claude-opus-5.5	Mini-SWE-Agent 2.2.8	Claude Opus 5.5	10 MB
mini-swe-agent__gpt-5.5	Mini-SWE-Agent 2.2.8	GPT-5.5	17 MB
mini-swe-agent__gpt-5.4	Mini-SWE-Agent 2.2.8	GPT-5.4	103 MB
mini-swe-agent__gpt-5.3-codex	Mini-SWE-Agent 2.2.8	GPT-5.3-Codex	18 MB
mini-swe-agent__mimo-v2.5-pro	Mini-SWE-Agent 2.2.8	MiMo v2.5 Pro	32 MB
mini-swe-agent__kimi-k2.6	Mini-SWE-Agent 2.2.8	Kimi K2.6	30 MB
mini-swe-agent__mimo-v2.5	Mini-SWE-Agent 2.2.8	MiMo v2.5	34 MB
mini-swe-agent__deepseek-v4-flash	Mini-SWE-Agent 2.2.8	DeepSeek V4 Flash	26 MB
mini-swe-agent__gpt-5.2-codex	Mini-SWE-Agent 2.2.8	GPT-5.2-Codex	13 MB
mini-swe-agent__hy3	Mini-SWE-Agent 2.2.8	Hunyuan3	20 MB
mini-swe-agent__gemini-3.5-flash	Mini-SWE-Agent 2.2.8	Gemini 3.5 Flash	29 MB
mini-swe-agent__o4-mini	Mini-SWE-Agent 1.14.4, 2.2.8	o4-mini	23 MB
codex-cli__gpt-5.2-codex	Codex CLI 0.128.0	GPT-5.2-Codex	7 MB
codex-cli__o4-mini	Codex CLI 0.128.0	o4-mini	8 MB
opencode__gpt-5.2-codex	OpenCode 1.4.6	GPT-5.2-Codex	12 MB
opencode__o4-mini	OpenCode 1.4.6	o4-mini	6 MB

Run directories

Each archive unpacks to <config>/<bug>/run-NN/. Run IDs are the original ones. The paper scores the first three attempts per bug with a schema-valid diagnosis, so a gap in the numbering means an earlier attempt ended without one. Every run directory has:

• diagnosis.json: the final diagnosis (root-cause blocks and an explanation)
• diagnosis_scores.json: the ground truth, the diagnosis, and its scores
• session_stats.json: API calls, tokens, cost, and wall time

The trace depends on the harness:

Harness	Trace files
Mini-SWE-Agent	trajectory.json: the run configuration and every model message, tool call, and tool output
Codex CLI	codex_exec.log (Codex's JSON event stream) and codex_last_message.json
OpenCode	prompt.txt and opencode_cli.log (OpenCode's JSON event stream)

Attempts table

Column	Description
config, harness, model	Configuration (archive name), harness, and model
bug, run, attempt	Benchmark instance, run ID, and position (1–3) among the scored attempts
harness_version	Harness version recorded in the trace
workspace	anonymized or legacy. Legacy attempts ran before workspace paths were anonymized, so their workspace path contains the bug ID.
has_trace	Whether the run directory contains a trace file
num_blocks	Number of root-cause blocks in the diagnosis
file_coverage … mention_coverage	The nine accuracy metrics, in [0, 1]
api_calls … duration_seconds	session_stats.json fields
diagnosis	The raw diagnosis.json text

from datasets import load_dataset

attempts = load_dataset("OrderLab/distdebug-bench-traces", split="train").to_pandas()
per_bug = attempts.groupby(["config", "bug"])["block_coverage"].mean()
print(per_bug.groupby("config").mean().sort_values(ascending=False))

Notes:

• Nine attempts submitted a diagnosis with no blocks. They count as zero on every metric, as in the paper.
• mini-swe-agent__o4-mini ray-60991/run-01 has no trace file.
• Codex CLI records one API call and no cost per run.
• Claude Opus 5.5's thinking text is hidden, so output_reasoning_tokens is
  0. Thinking tokens are billed as output tokens, so total_tokens and cost are correct.

Reproducing the paper

The GitHub repository rescores every diagnosis with the public evaluator, aggregates the scores, and rebuilds Table 1 and Figures 2 and 3. It then checks the results against the paper's:

git clone https://github.com/OrderLab/distdebug-bench && cd distdebug-bench
pip install -e ".[paper]"
hf download OrderLab/distdebug-bench-traces --repo-type dataset --local-dir traces
python paper/reproduce.py traces

Redactions

The traces are otherwise unmodified, except for these replacements:

• Local paths, the machine's host name and user name, a private API endpoint, an internal project name, and one git author line are replaced with the placeholders /home/user, host, user, <azure-endpoint>, harness, and user <user@example.com>.
• Encrypted reasoning items returned by the OpenAI Responses API (encrypted_content) are set to null. They are opaque and cannot be decrypted.

Rescoring the redacted diagnoses reproduces every stored score.

License

The diagnoses, scores, and metadata are under the MIT license. Tool outputs in the traces quote source code, git history, and issue text from the 11 upstream systems, which remain under their own licenses. See the benchmark card for the list.

Citation

@inproceedings{jiang2026distdebugbench,
  title     = {{DistDebug-Bench}: Can {LLM} Agents Diagnose Bugs in Distributed Systems?},
  author    = {Jiang, Yuxuan and Liu, Chenglin and Rose, John and Miller, Mason and Thavamani, Ruthesh and Wu, Juntao and Huang, Yinwei and Xu, Jiahao and Huang, Peng},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
  year      = {2026}
}