---
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:

```python
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:

```python
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

```bibtex
@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}
}
```