pretty_name: LongHarness
language:
- en
multilinguality:
- monolingual
size_categories:
- n<1K
task_categories:
- question-answering
- text-classification
tags:
- long-context
- benchmark
- language-model-agents
- agent-evaluation
- retrieval
- reasoning
- code
- arxiv:2609.38137
LongHarness Bench: Stress-Testing Language Model Harnesses for Long-Context Reasoning
LongHarness evaluates how language-model harnesses access and reason over long contexts. It is designed to distinguish context-access strategies, including direct reading, lexical and semantic retrieval, iterative agents, and recursive language-model harnesses. The benchmark contains 200 evaluation instances across four task suites.
Benchmark Tasks
| Task | Context | Required output | Instances |
|---|---|---|---|
| Constraint Solving Search | Documents about 160 people and a query containing three to five conditions | The five person IDs satisfying every condition | 50 |
| Equivalent Program Pair Search | 200 anonymous Python programs with semantically confusable mutants | Every semantically equivalent program pair | 50 |
| Program Execution Tracing | 320 shuffled computation cells and an eight-step final query | The final structured result and eight supporting cell IDs | 50 |
| Outlier Memo Detection | 700 memos containing 2,500 statements | The 15 memo IDs containing conflicting claims | 50 |
Every task uses exact instance accuracy. Partial outputs, missing items, and extra items are incorrect under the primary metric. Program Execution Tracing also reports answer-only accuracy as an auxiliary metric.
Download
LongHarness is distributed as raw Markdown contexts, JSON answer keys, and JSONL manifests. Download the complete repository snapshot with:
hf download StringNLP/longharness \
--repo-type dataset \
--local-dir longharness
This release is not packaged as a row-oriented datasets.Dataset. Preserving
the original context documents and keeping answer keys outside the agent's
workspace are part of the evaluation protocol.
Repository Structure
README.md
RELEASE.json
DATA_CHECKSUMS.sha256
manifest.jsonl
score.py
tools/verify_release.py
tasks/
<task-id>/
README.md
manifest.jsonl
contexts/sample-00000.md
answers/sample-00000.json
The root manifest contains all 200 instances. Each row identifies the task, instance index, context path, answer path, context byte count, and SHA-256 digest. Task manifests may include additional task-specific statistics.
Evaluation Protocol
Expose only the selected context and task instruction to the evaluated system.
Do not expose answers/, manifests containing answer paths, the scorer, or
other instances from the repository. Answer files are public for reproducible
scoring, so results should be treated as benchmark evaluation rather than
closed-test assessment.
Predictions are JSON Lines records with task, index, and prediction:
{"task":"constraint-solving-search","index":0,"prediction":{"matches":["P-018","P-076","P-096","P-101","P-155"]}}
The prediction value has a task-specific schema:
- Constraint Solving Search:
{"matches": [...]} - Equivalent Program Pair Search:
[[cell_a, cell_b], ...] - Program Execution Tracing:
{"answer": {...}, "evidence": [...]} - Outlier Memo Detection:
["M-001", "M-002", ...]
Run the official scorer from the downloaded snapshot:
cd longharness
python score.py predictions.jsonl --output scores.json
The scorer reports exact and answer-only accuracy per task, plus macro-average exact accuracy across the four task suites.
Integrity Check
Validate all manifests, files, checksums, and task counts before evaluation:
cd longharness
python tools/verify_release.py
DATA_CHECKSUMS.sha256 and the digests in RELEASE.json provide additional
snapshot-level integrity information.
Construction
Each instance is generated from a hidden structured specification before its public context is rendered. The specification is a predicate world, relation graph, computation graph, or executable program family, depending on the task. A task-specific solver computes the gold answer, controlled mutations create hard distractors, and deterministic validators recompute the answer and verify the intended role of every required item.
Equivalent Program Pair Search is adapted from standard-input APPS problems. Its answer keys represent agreement over the retained valid-input test suites and additional checks; they are not formal proofs of equivalence over arbitrary Python inputs.
Intended Use
LongHarness is intended for evaluating the accuracy and efficiency of systems that process long contexts, especially agent harnesses and retrieval-augmented reasoning systems. It is not intended as a training corpus, a measure of general intelligence, or a substitute for evaluation on natural user workloads.
When reporting results, include the model, harness, task-level exact accuracy, macro-average exact accuracy, token accounting method, and execution-cost assumptions. Harness configurations and tool access can materially affect both accuracy and cost.
Limitations
- The benchmark contains constructed evaluation environments rather than naturally occurring user sessions.
- It covers English documents and Python programs only.
- Exact-set scoring can understate progress on partially correct responses.
- Public answer keys make contamination possible; do not use this release for training or expose answer-bearing files during inference.
- Program equivalence is validated behaviorally over explicit input contracts and tests, not proven for every possible Python object.
- Performance on these 200 instances should not be interpreted as a complete measure of long-context capability.
Citation
Please cite the LongHarness Bench paper:
@misc{pham2026longharness,
title = {{LongHarness Bench}: Stress-Testing Language Model Harnesses for Long-Context Reasoning},
author = {Pham, Quang Hieu and Nguyen, Thuy Duong and Chen, Jocelyn Qiaochu and Ye, Xi},
year = {2026},
eprint = {2609.38137},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2609.38137}
}