README.md

metadata

pretty_name: AgentWebBench Corpus
license: mit
language:
  - en
task_categories:
  - text-retrieval
size_categories:
  - 10M<n<100M
tags:
  - information-retrieval
  - dense-retrieval
  - faiss
  - embeddings
  - clueweb22
  - agents
  - benchmark

AgentWebBench Corpus

Pre-built dense-retrieval corpus for AgentWebBench [ICML 2026], a benchmark for Multi-Agent Coordination in Agentic Web over a realistic 100-website slice of ClueWeb22 (~18.4M documents).

This repository holds the embeddings and FAISS indices the benchmark loads at run time, including per-website indices, a global index, and website-level vectors. It does not contain ClueWeb22 text (see Raw documents).

• Websites: 100
• Documents: ~18.4M
• Embedding dim: 1024

⚠️ Derived from ClueWeb22. These vectors and ID maps are derived from ClueWeb22 documents. Use is subject to the ClueWeb22 license.

1. Contents

faiss_indices/
├── <website>.faiss            # per-website FAISS index (IndexFlatIP over doc vectors)
└── <website>_doc_ids.npy      # row i of the index → that website's ClueWeb22 doc_id
website_embeddings.pkl         # {website → 1024-d vector}; used to rank/select websites
global_doc_index.faiss         # one FAISS index over ALL ~18.4M documents
global_doc_ids.npy             # row i of the global index → ClueWeb22 doc_id

How .faiss and _doc_ids.npy pair up: a FAISS search returns integer row positions, not document IDs. The .faiss stores the vectors; the _doc_ids.npy is the row→doc_id lookup. They are strictly aligned by position and must be used together.

2. Usage

2.1 With the AgentWebBench code (recommended)

git clone https://github.com/cxcscmu/AutoGEO/AgentWebBench
cd AgentWebBench
python -m awbench.download --output-dir ./AgentWebBench-corpus
# then set WEBSITE_DATA_ROOT in awbench/config.py to ./AgentWebBench-corpus

2.2 Directly with huggingface_hub

from huggingface_hub import snapshot_download

path = snapshot_download(
    repo_id="cx-cmu/AgentWebBench-corpus",
    repo_type="dataset",
    local_dir="./AgentWebBench-corpus",
)

2.3 Load an index

import faiss, numpy as np

index = faiss.read_index("faiss_indices/community.spiceworks.com.faiss")
doc_ids = np.load("faiss_indices/community.spiceworks.com_doc_ids.npy", allow_pickle=True)

# encode your query with MiniCPM-Embedding-Light (1024-d, normalized), then:
scores, rows = index.search(query_vec.reshape(1, -1).astype("float32"), 10)
hits = [str(doc_ids[i]) for i in rows[0]]   # ClueWeb22 doc_ids

This corpus stores only vectors and doc_ids. To read the actual page text for a retrieved doc_id, obtain ClueWeb22 category B (license required) and point CLUEWEB_ROOT_PATH at it.

3. How it was built

We follow tevatron to build embeddings and FAISS indices. Specifically,

• Encoder: openbmb/MiniCPM-Embedding-Light
• Dimension: 1024
• Index: FAISS IndexFlatIP

4. Citation

@article{zhong2026agentwebbench,
  title={AgentWebBench: Benchmarking Multi-Agent Coordination in Agentic Web},
  author={Zhong, Shanshan and Shen, Kate and Xiong, Chenyan},
  journal={arXiv preprint arXiv:2604.10938},
  year={2026}
}