Datasets:
File size: 3,653 Bytes
aedfe47 acc6680 aedfe47 acc6680 7e0bd39 acc6680 dedce64 acc6680 baf2d49 ab0a219 baf2d49 acc6680 baf2d49 4ff14e6 c367d8e 4ff14e6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | ---
license: cc-by-4.0
task_categories:
- other
tags:
- agents
- evaluation
- benchmarks
- leaderboards
size_categories:
- 10K<n<100K
---
# HAL Agent Evaluations Response Matrix
A cleaned, task-level response matrix built from the [HAL Leaderboard traces](https://huggingface.co/datasets/agent-evals/hal_traces), released as a single flat CSV. This is the dataset used in [*Agent Evaluation Reliability: More Tasks Won't (Always) Fix an Agent Leaderboard*](https://arxiv.org/abs/2610.00651). The data covers **+32k task-level evaluations** across **9 benchmarks**. Each benchmark is run with 2–3 agent scaffolds and 13–34 models.
## Usage
Each row is one evaluation: a single model, running in a single scaffold, attempting a single task. An agent is identified by the combination of `Model_Name`, `Scaffold_Name`, and `Reasoning_Effort`.
| Column | Type | Description |
|--------------------|--------|--------------------------------------------------------------------|
| `Run_id` | string | Id associated with the original hal_leaderboard trace. |
| `Benchmark` | string | Benchmark name |
| `Task_id` | string | Task ID within the benchmark |
| `Model_Name` | string | Underlying LLM |
| `Scaffold_Name` | string | Agent scaffold (harness) |
| `Reasoning_Effort` | string | Reasoning-effort setting (`low`, `medium`, `high`); empty if N/A |
| `Score` | float | Task score in [0, 1] |
Load it with the `datasets` library, then pivot to get a response matrix for a single benchmark, with agents as rows and tasks as columns:
```python
from datasets import load_dataset
df = load_dataset("razam2/hal-response-matrix")["train"].to_pandas()
gaia = df[df["Benchmark"] == "GAIA"]
matrix = gaia.pivot_table(
index=["Model_Name", "Scaffold_Name"],
columns="Task_id",
values="Score",
)
```
This is an unofficial, community-processed release. Please cite the original HAL paper (Kapoor et al., ICLR 2026) alongside this dataset.
```
@misc{azam2026halresponsematrix,
author = {Azam, Ruhana},
title = {{HAL} Response Matrix: Item-Level Results from the Holistic Agent Leaderboard},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/datasets/razam2/hal-response-matrix}},
note = {Processed from the Holistic Agent Leaderboard \citep{kapoor2026hal}}
}
@inproceedings{kapoor2026hal,
title = {Holistic Agent Leaderboard: The Missing Infrastructure for {AI} Agent Evaluation},
author = {Kapoor, Sayash and Stroebl, Benedikt and Kirgis, Peter and Nadgir, Nitya and
Siegel, Zachary S. and Wei, Boyi and Xue, Tianci and Chen, Ziru and Chen, Felix and
Utpala, Saiteja and Ndzomga, Franck and Oruganty, Dheeraj and Luskin, Sophie and
Liu, Kangheng and Yu, Botao and Arora, Amit and Hahm, Dongyoon and Trivedi, Harsh and
Sun, Huan and Lee, Juyong and Jin, Tengjun and Mai, Yifan and Zhou, Yifei and
Zhu, Yuxuan and Bommasani, Rishi and Kang, Daniel and Song, Dawn and
Henderson, Peter and Su, Yu and Liang, Percy and Narayanan, Arvind},
booktitle = {International Conference on Learning Representations (ICLR)},
year = {2026},
url = {https://arxiv.org/abs/2510.11977}
}
```
|