---
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",
)