| --- |
| 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", |
| ) |
