AgenticSearchQueryset/ASQ

Dataset

πŸ“– Dataset Description

3

3 commits

2 linked in READMEs

updated Apr 1, 2026

See the code

README

πŸ€– ASQ: Agentic Search Queryset

A dataset capturing RAG agents' search behaviours.

Paper Repository License: MIT


πŸ“– Dataset Description

ASQ (Agentic Search Queryset) is a dataset designed to capture the search behaviors of the RAG agents. It collects intermediate synthetic queries, retrieved documents, and thoughts (reasoning descriptions) produced or consumed by agents.

πŸ“Š Dataset Statistics

  • 615k traces (0.12% incomplete)
  • 614k answers
  • 680k synthetic queries
  • 680k retrieved ranked lists
  • 3 diverse agent settings
  • 2 diverse retrieval settings

πŸ—οΈ Data Construction

Details about dataset construction are available on the paper: arXiv preprint.

See our GitHub repository to reproduce the construction of ASQ, or to extend it:github.com/fpezzuti/ASQ.


πŸ“‚ Dataset Organisation

The ASQ dataset is organised hierarchically under the traces/ directory:

traces/
└── dataset/
    └── retriever_config/
        └── agent_family/
            └── model/
                β”œβ”€β”€ answers.tsv
                β”œβ”€β”€ iter_queries.tsv
                β”œβ”€β”€ retrieved_docs.tsv
                └── thoughts.tsv

Placeholders:

  • dataset: the base dataset from which traces were collected (e.g., HotpotQA-test).
  • retriever_config: retrieval pipeline's configuration (e.g., BM25_k100_electra_k3).
  • agent_family: type of agent generating the traces (e.g., Autorefine).
  • model: generator variant (e.g., Qwen-7B).

πŸ“¦ Artifacts

Each collection of traces comprises four TSV artifact files, with rows associated with a qid of the organic query:

ArtifactDescription
answers.tsvAnswers generated by the agent for each query.
iter_queries.tsvSynthetic queries generated by the agent per query and iteration.
retrieved_docs.tsvRanked list of documents retrieved per query and iteration.
thoughts.tsvCoT reasoning "thoughts" of the agent per query and iteration.

🏁 Answers

Columns:

  • qid (string): qid of the organic query.
  • answer (string): answer generated by the agent.

✍️ Synthetic Queries

Columns:

  • qid (string): qid of the organic query.
  • iteration (integer): iteration number within agent's inference loop.
  • llm_query (string): synthetic query generated at that iteration.

πŸ” Retrieved Documents

Columns:

  • qid (string): qid of the organic query.
  • iteration (integer): iteration number within the agent's inference loop.
  • docid (integer): identifier of the retrieved document.
  • rank (integer): rank of the document in the ranked list retrieved during that iteration.

πŸ’­ Thoughts

Columns:

  • qid (string): qid of the organic query.
  • iteration (integer): iteration number within the agent's inference loop.
  • thought (string): chain-of-thought thought produced by the agent at that iteration.

πŸš€ Usage

Please see our GitHub repository:github.com/fpezzuti/ASQ.


βš–οΈ License

The ASQ dataset is released under the MIT License. Individual source datasets may have their own licenses.


πŸ› οΈ Ethics Statement

ASQ is derived from publicly available datasets and is intended solely for research on agentic search behaviour. The authors do not endorse or assume responsibility for the content or any biases present in the traces. The contents of these traces should not be interpreted as representing the views of the researchers or their institutions. Users are advised to apply safety and content filters when using them.


πŸ”— Citation

If you find our work useful, please cite it as follows:

@misc{fpezzuti2026asq,
      title={A Picture of Agentic Search}, 
      author={Pezzuti, Francesca and Frieder, Ophir and Silvestri, Fabrizio and MacAvaney, Sean and Tonellotto, Nicola},
      year={2026},
      eprint={2602.17518},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/pdf/2602.17518}, 
}
agent
ir
retrieval
search
text-generation

Contributors

fpezzuti

3 commits

AgenticSearchQueryset/ASQ

Dataset

πŸ“– Dataset Description

3

3 commits

2 linked in READMEs

updated Apr 1, 2026

See the code

README

πŸ€– ASQ: Agentic Search Queryset

A dataset capturing RAG agents' search behaviours.

Paper Repository License: MIT


πŸ“– Dataset Description

ASQ (Agentic Search Queryset) is a dataset designed to capture the search behaviors of the RAG agents. It collects intermediate synthetic queries, retrieved documents, and thoughts (reasoning descriptions) produced or consumed by agents.

πŸ“Š Dataset Statistics

  • 615k traces (0.12% incomplete)
  • 614k answers
  • 680k synthetic queries
  • 680k retrieved ranked lists
  • 3 diverse agent settings
  • 2 diverse retrieval settings

πŸ—οΈ Data Construction

Details about dataset construction are available on the paper: arXiv preprint.

See our GitHub repository to reproduce the construction of ASQ, or to extend it:github.com/fpezzuti/ASQ.


πŸ“‚ Dataset Organisation

The ASQ dataset is organised hierarchically under the traces/ directory:

traces/
└── dataset/
    └── retriever_config/
        └── agent_family/
            └── model/
                β”œβ”€β”€ answers.tsv
                β”œβ”€β”€ iter_queries.tsv
                β”œβ”€β”€ retrieved_docs.tsv
                └── thoughts.tsv

Placeholders:

  • dataset: the base dataset from which traces were collected (e.g., HotpotQA-test).
  • retriever_config: retrieval pipeline's configuration (e.g., BM25_k100_electra_k3).
  • agent_family: type of agent generating the traces (e.g., Autorefine).
  • model: generator variant (e.g., Qwen-7B).

πŸ“¦ Artifacts

Each collection of traces comprises four TSV artifact files, with rows associated with a qid of the organic query:

ArtifactDescription
answers.tsvAnswers generated by the agent for each query.
iter_queries.tsvSynthetic queries generated by the agent per query and iteration.
retrieved_docs.tsvRanked list of documents retrieved per query and iteration.
thoughts.tsvCoT reasoning "thoughts" of the agent per query and iteration.

🏁 Answers

Columns:

  • qid (string): qid of the organic query.
  • answer (string): answer generated by the agent.

✍️ Synthetic Queries

Columns:

  • qid (string): qid of the organic query.
  • iteration (integer): iteration number within agent's inference loop.
  • llm_query (string): synthetic query generated at that iteration.

πŸ” Retrieved Documents

Columns:

  • qid (string): qid of the organic query.
  • iteration (integer): iteration number within the agent's inference loop.
  • docid (integer): identifier of the retrieved document.
  • rank (integer): rank of the document in the ranked list retrieved during that iteration.

πŸ’­ Thoughts

Columns:

  • qid (string): qid of the organic query.
  • iteration (integer): iteration number within the agent's inference loop.
  • thought (string): chain-of-thought thought produced by the agent at that iteration.

πŸš€ Usage

Please see our GitHub repository:github.com/fpezzuti/ASQ.


βš–οΈ License

The ASQ dataset is released under the MIT License. Individual source datasets may have their own licenses.


πŸ› οΈ Ethics Statement

ASQ is derived from publicly available datasets and is intended solely for research on agentic search behaviour. The authors do not endorse or assume responsibility for the content or any biases present in the traces. The contents of these traces should not be interpreted as representing the views of the researchers or their institutions. Users are advised to apply safety and content filters when using them.


πŸ”— Citation

If you find our work useful, please cite it as follows:

@misc{fpezzuti2026asq,
      title={A Picture of Agentic Search}, 
      author={Pezzuti, Francesca and Frieder, Ophir and Silvestri, Fabrizio and MacAvaney, Sean and Tonellotto, Nicola},
      year={2026},
      eprint={2602.17518},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/pdf/2602.17518}, 
}
agent
ir
retrieval
search
text-generation

Contributors

fpezzuti

3 commits