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PerspectiveGap

Tasks:
Text Generation
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json
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benchmark
theory-of-mind
multi-agent
information-management
orchestration
prompt-engineering
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metadata 
pretty_name: PerspectiveGap
license: mit
language:
  - en
annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
multilinguality:
  - monolingual
source_datasets:
  - original
task_categories:
  - text-generation
tags:
  - benchmark
  - theory-of-mind
  - multi-agent
  - information-management
  - orchestration
  - prompt-engineering
  - arxiv:2606.08878
  - text
  - jsonl
  - datasets
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: test
        path: evaluations.jsonl

Dataset Card for PerspectiveGap

PerspectiveGap is a benchmark for evaluating LLMs' ability to compose orchestration prompts for multi-agent systems. It tests whether a model can decide what each sub-agent in a multi-agent workflow needs to know, without leaking irrelevant context.

Paper: PerspectiveGap: A Benchmark for Multi-Agent Orchestration Prompting

Code and scorers: WhymustIhaveaname/PerspectiveGap

Interactive leaderboard: sun1245/PerspectiveGap-Leaderboard

Project collection: PerspectiveGap Benchmark

This Hugging Face dataset contains the released rendered set: 220 rows from 110 scenarios rendered with seeds 1 and 42. Each row includes the two task prompts, visible fragments, distractor ID, and answer key.

Dataset Details

Dataset Description

The dataset is released as a single test split. It is intended for benchmarking prompt composition and context filtering in multi-agent orchestration settings.

Dataset Sources

The released JSONL is deterministically rendered from the source scenarios in the GitHub repository. Three generic prompt-engineering distractor fragments are included in the source repository with source URLs recorded in their markdown frontmatter; preserve those attributions if you redistribute modified source files.

Uses

Direct Use

Use PerspectiveGap to evaluate models or prompting systems on two tasks:

  1. Role-fragment assignment: select the visible fragment IDs that belong in each sub-agent prompt.
  2. Prompt writing: write one prompt per sub-agent while including only the needed fragments.

The accompanying GitHub repository contains scripts for rendering model requests and scoring predictions.

Out-of-Scope Use

Do not use this test set, including reference_need_sets or distractor_id, as model training data or as an in-context demonstration set when reporting benchmark results. The dataset is not designed to represent all possible multi-agent architectures, application domains, or safety requirements.

Dataset Structure

Data Splits

split rows scenarios shuffle seeds
test 220 110 1, 42

Data Fields

field meaning
evaluation_id stable row ID
scenario_id source scenario ID
shuffle_seed seed used for distractor sampling and fragment order
roles roles that need prompts
fragments visible fragments shown to the model
distractor_id visible fragment ID of the distractor
reference_need_sets answer key in visible fragment IDs
role_assignment_prompt prompt for the JSON assignment task
prompt_writing_prompt prompt for the free-form writing task

distractor_id is already in the visible ID space, so no relabel map is needed. Each dataset row contains both task prompts. The reference runner in the GitHub repository sends one model request per selected task.

Loading 

from datasets import load_dataset

ds = load_dataset("sun1245/PerspectiveGap", split="test")
print(ds[0]["evaluation_id"])

If you mirror this dataset under another namespace, replace sun1245/PerspectiveGap with that dataset repository ID.

Evaluation 

git clone https://github.com/WhymustIhaveaname/PerspectiveGap.git
cd PerspectiveGap
uv sync

# Score the bundled example without any API key.
uv run python scripts/score_predictions.py --predictions tests/fixtures/example_predictions.jsonl

To run a model, set the relevant provider API key and use scripts/run_model_predictions.py; see the GitHub README for provider names and environment variables.

Dataset Creation

The benchmark scenarios were curated to test information routing decisions in multi-agent workflows. For the released Hugging Face file, each source scenario is rendered with two deterministic shuffle seeds. Rendering injects one generic distractor fragment, shuffles the visible fragments, relabels them into the visible f1, f2, ... ID space, and emits both task prompts plus the answer key.

Evaluation Notes

  • PerspectiveGap is an answer-keyed benchmark for multi-agent orchestration prompting and context routing.
  • The released scenarios are curated to stress role-specific information selection, distractor resistance, and prompt composition across diverse orchestration topologies.
  • The included answer keys make scoring transparent and auditable.
  • The prompt-writing scorer in the GitHub repository is deterministic, fast, and reproducible; it measures whether generated prompts include the needed fragments while excluding irrelevant ones.
  • For benchmark reporting, use the dataset as a held-out evaluation set and follow the rendering and scoring scripts in the GitHub repository.

More Information

Citation 

@misc{sun2026perspectivegapbenchmarkmultiagentorchestration,
      title={PerspectiveGap: A Benchmark for Multi-Agent Orchestration Prompting}, 
      author={Youran Sun and Xingyu Ren and Kejia Zhang and Xinpeng Liu and Jiaxuan Guo},
      year={2026},
      eprint={2606.08878},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2606.08878}, 
}