README.md

metadata

license: mit
task_categories:
  - text-generation
  - question-answering
language:
  - en
tags:
  - benchmark
  - agentic
  - llm-agents
  - tool-use
  - simulation
  - physics
  - high-energy-physics
  - particle-physics
  - LHC
pretty_name: Collider-Bench
size_categories:
  - n<1K
dataset_info:
  features:
    - name: task_id
      dtype: string
    - name: paper_id
      dtype: string
    - name: task_type
      dtype: string
    - name: analysis_target
      dtype: string
    - name: signal_model
      dtype: string
    - name: observable
      dtype: string
    - name: observable_pretty
      dtype: string
    - name: plot_units
      dtype: string
    - name: score_mode
      dtype: string
    - name: walltime
      dtype: string
    - name: difficulty
      dtype: string
    - name: tags
      list: string
    - name: initial_prompt
      dtype: string
    - name: task_md
      dtype: string
    - name: template_yaml
      dtype: string
    - name: n_bins
      dtype: int64
    - name: paper_pdf
      dtype: binary
    - name: paper_pdf_sha256
      dtype: string
    - name: paper_pdf_bytes
      dtype: int64
    - name: object_efficiencies
      list:
        - name: filename
          dtype: string
        - name: data
          dtype: binary
        - name: sha256
          dtype: string
        - name: size_bytes
          dtype: int64
  splits:
    - name: train
      num_bytes: 7635433
      num_examples: 10
  download_size: 6721991
  dataset_size: 7635433
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Collider-Bench is an AI benchmark for evaluating whether LLM agents can reproduce experimental analyses from the Large Hadron Collider (LHC) at CERN using only public papers and open scientific software.

Each task requires multi-step scientific reasoning by an autonomous coding agent, from reading a published CMS or ATLAS search and identifying the relevant signal region, to generating and processing simulated signal events, implementing the event selection, and predicting the binned signal yields reported by the analysis

The benchmark tests long-horizon scientific reasoning under realistic conditions, including ambiguous or underspecified paper descriptions, underdocumented domain-specific tools and approximate public simulation pipelines.

This HuggingFace dataset hosts the task corpus only — the agent-facing instructions and artifacts. The runtime harness, scorer, and hidden reference values live in the companion GitHub repository:

🔗 https://github.com/dfaroughy/Collider-Bench

The reference yields used by the scorer are deliberately not published here to preserve the benchmark's blind-test property.

Task schema

Field	Type	Description
task_id	string	Canonical task identifier, e.g. sus-16-046_sim-T5Wg
paper_id	string	CMS analysis identifier, e.g. CMS-SUS-16-046
task_type	string	Task family (simulation for the sim corpus)
analysis_target	string	Final state under study (photons, single lepton, leptons + jets)
signal_model	string	SUSY simplified-model name + slice (T5Wg, high-H_T, T2tt, compressed, …)
observable	string	Observable key as used by the scorer (STgamma, MET, …)
observable_pretty	string	LaTeX-style label (S_T^gamma, E_T^miss, p_T^miss)
plot_units	string	y-axis units of the histogram (Events/bin, Events/GeV)
score_mode	string	Scoring mode used by the harness (shape_norm for sim tasks)
walltime	string	Harness walltime budget (e.g. 2h30m)
difficulty	string	easy / medium / hard rough difficulty tag
tags	seq	Free-form metadata tags
initial_prompt	string	The system prompt the reference simple agent receives (from agents/simple/run.py:build_prompt); use as a starting point or replace with your own
task_md	string	The full TASK.md text — the agent's primary instructions
template_yaml	string	Null-filled HEPData-style YAML the agent fills with predicted bin values
n_bins	int64	Total bin count across all dependent variables in the template
paper_pdf	binary	Bytes of the CMS analysis paper (publicly available on CDS/Inspire-HEP)
paper_pdf_sha256	string	SHA-256 of paper_pdf
paper_pdf_bytes	int64	Length of paper_pdf
object_efficiencies	seq<{filename, data, sha256, size_bytes}>	CMS public detector-efficiency maps (ROOT files) the agent needs to apply during selection

Task corpus

Task id	Analysis target	Signal	Observable	Paper
sus-16-034_sim-TChiWZ	leptons + jets	TChiWZ	$E_T^{\rm miss}$	CMS-SUS-16-034
sus-16-046_sim-T5Wg	photons	T5Wg	$S_T^{\gamma}$	CMS-SUS-16-046
sus-16-046_sim-TChiWg	photons	TChiWg	$S_T^{\gamma}$	CMS-SUS-16-046
sus-16-047_sim-T5Wg_highHT	photons	T5Wg, high-$H_T$	$p_T^{\rm miss}$	CMS-SUS-16-047
sus-16-047_sim-T5Wg_lowHT	photons	T5Wg, low-$H_T$	$p_T^{\rm miss}$	CMS-SUS-16-047
sus-16-047_sim-T6gg_highHT	photons	T6gg, high-$H_T$	$p_T^{\rm miss}$	CMS-SUS-16-047
sus-16-047_sim-T6gg_lowHT	photons	T6gg, low-$H_T$	$p_T^{\rm miss}$	CMS-SUS-16-047
sus-16-051_sim-T2tt_SRG	single lepton	T2tt	$E_T^{\rm miss}$	CMS-SUS-16-051
sus-16-051_sim-T2bW_SRG	single lepton	T2bW	$E_T^{\rm miss}$	CMS-SUS-16-051
sus-16-051_sim-T2tt_comp	single lepton	T2tt, compressed	$E_T^{\rm miss}$	CMS-SUS-16-051

Citation

If you use Collider-Bench in your research, please cite:

@misc{colliderbench2026,
  title  = {Collider-Bench: A benchmark for LHC analysis recasting by LLM agents},
  author = {Faroughy, Darius A. and Palacios Schweitzer, Sofia and Pang, Ian and Mishra-Sharma, Siddharth and Shih, David},
  year   = {2026},
  url    = {https://huggingface.co/datasets/Dariusfar/ColliderBench},
}

License

MIT (matches the GitHub repo). The CMS paper PDFs and detector efficiency maps are reproduced here as published by the CMS Collaboration under the terms of their respective public-data policies.