razam2/hal-response-matrix

HAL Agent Evaluations Response Matrix

A cleaned, task-level response matrix built from the HAL Leaderboard 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. 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:

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}
}