README.md

metadata

license: other
language:
  - en
  - zh
size_categories:
  - n<1K
task_categories:
  - question-answering
  - text-generation
task_ids:
  - document-question-answering
  - open-book-qa
tags:
  - benchmark
  - long-context
  - multi-hop-reasoning
  - source-internal-reasoning
  - evidence-withheld
  - document-qa
  - mlcroissant
  - neurips-2026
annotations_and_code_license: cc-by-4.0
source_corpus_license: mixed
pretty_name: WildTrace strict481
configs:
  - config_name: with_answers
    data_files:
      - split: test
        path: data/wildtrace_strict481.with_answers.jsonl
  - config_name: questions_only
    data_files:
      - split: test
        path: data/wildtrace_strict481.questions_only.jsonl

WildTrace strict481

WildTrace is a source-internal long-context multi-hop reasoning benchmark built from natural evidence trails. Unlike reverse-synthetic QA, its tasks are mined in situ from long source documents before questions are written. The strict481 release contains 481 locked tasks over 214 public long-form sources, with full-document, evidence-withheld evaluation. The model under test receives only the source document and the public question; evidence spans, clue counts, reference answers, rubrics, and validation notes are hidden.

Freeze id: wildtrace_strict481_public_20260710. Release version: wildtrace-strict481-public-2026-07-10. Public task and source identifiers are opaque and stable within this release.

Contents

• data/wildtrace_strict481.with_answers.json: full nested benchmark artifact with questions, ground truth, rubrics, and evidence clues.
• data/wildtrace_strict481.with_answers.jsonl: Hugging Face / Arrow-friendly view of the same rows, with ground_truth and clues serialized as JSON strings.
• data/wildtrace_strict481.questions_only.json and .jsonl: model-input rows without answers/rubrics/clues.
• corpus/*.txt: the 214 source documents referenced by the tasks.
• eval/run_eval.py, eval/run_judge.py, eval/config.json: minimal evidence-withheld evaluation and rubric-judge harness.
• methodology/EVAL_PROTOCOL.md: prompt templates, generation settings, context policy, judge prompt, scoring normalization, and aggregation rules.
• metadata/: schema, corpus manifest, release manifest, checksums, and dataset statistics.
• croissant.json: MLCommons Croissant metadata with Responsible AI fields.

Snapshot Statistics

Item	Value
Tasks	481
Source documents	214
Languages	en: 441, zh: 40
Min estimated document tokens	19,106
Median estimated document tokens	302,084
Max estimated document tokens	2,549,520

Evidence Geometries

Geometry	Count
abductive_inference	65
causal_attribution	67
comparative	67
counterfactual_reasoning	67
forward_chain	73
intersection_query	71
temporal_reconstruction	71

Context Tiers

Tier	Count
L0_<=128K	90
L1_128K_181K	59
L2_181K_256K	61
L3_256K_362K	58
L4_362K_512K	59
L5_512K_724K	58
L6_724K_1M	93
L7_>1M	3

Source Families

Source family	Task count
cjk_literature	40
en_literature	318
technical_report	123

Usage

Local package:

from datasets import load_dataset

ds = load_dataset(
    "json",
    data_files="hf_release_wildtrace_strict481_20260621/data/wildtrace_strict481.with_answers.jsonl",
    split="train",
)
print(ds[0]["question_id"])
print(ds[0]["question_text"])

After upload to Hugging Face:

from datasets import load_dataset

ds = load_dataset("CinderD/wildtrace", "with_answers", split="test")

To evaluate a model, use eval/run_eval.py with --corpus ../corpus; the script constructs the evidence-withheld prompt from question_text and the full source document. Use eval/run_judge.py to score answers against the hidden rubric. See methodology/EVAL_PROTOCOL.md before reporting new results.

Scoring Denominators

When reporting model results, distinguish valid-response quality from coverage-sensitive all-task quality. The protocol document defines both views and the treatment of out-of-context or failed rows.

Quality Assurance

Each retained item is tied to source-grounded clues, a criterion-level rubric, and validation records used during construction. The public package strips internal iteration metadata while retaining the artifacts needed to inspect the released tasks. The final release passed checks for paradigm validity, leave-one-out multi-hop necessity, grounding, uniqueness, contamination resistance, and coherent-source constraints.

Responsible AI

The source corpus consists of public-domain literary works, Chinese literary sources, and publicly available technical incident reports. Some sources mention injury, death, violence, or distressing events because they are faithful to the original public documents. WildTrace is intended for diagnostic evaluation of source-grounded long-context reasoning, not for deployment certification in legal, medical, safety-critical, financial, or investigative settings.

Public release can increase benchmark contamination risk. New leaderboard claims should disclose whether a model may have seen the benchmark items or source documents during training.

License

WildTrace is a mixed-license release. Benchmark annotations, questions, rubrics, metadata, and scripts are released under CC BY 4.0. Source documents are not relicensed by WildTrace: they retain their original public-domain, public-agency, Project Gutenberg, or source-specific terms, which can vary by jurisdiction and by source. Users are responsible for checking source-specific terms before redistributing or adapting source texts.

Citation

@misc{wildtrace2026,
  title={WILDTRACE: Benchmarking Multi-Hop Reasoning over Natural Evidence Trails in Long Contexts},
  author={WildTrace authors},
  year={2026},
  howpublished={Hugging Face dataset},
  url={https://huggingface.co/datasets/CinderD/wildtrace},
  note={strict481 public release, freeze wildtrace_strict481_public_20260710}
}