A dataset capturing RAG agents' search behaviours.
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.
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.
The ASQ dataset is organised hierarchically under the traces/ directory:
traces/
βββ dataset/
βββ retriever_config/
βββ agent_family/
βββ model/
βββ answers.tsv
βββ iter_queries.tsv
βββ retrieved_docs.tsv
βββ thoughts.tsv
Placeholders:
Each collection of traces comprises four TSV artifact files, with rows associated with a qid of the organic query:
| Artifact | Description |
|---|---|
answers.tsv | Answers generated by the agent for each query. |
iter_queries.tsv | Synthetic queries generated by the agent per query and iteration. |
retrieved_docs.tsv | Ranked list of documents retrieved per query and iteration. |
thoughts.tsv | CoT reasoning "thoughts" of the agent per query and iteration. |
Columns:
qid (string): qid of the organic query.answer (string): answer generated by the agent.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.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.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.Please see our GitHub repository:github.com/fpezzuti/ASQ.
The ASQ dataset is released under the MIT License. Individual source datasets may have their own licenses.
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.
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},
}
3 commits
A dataset capturing RAG agents' search behaviours.
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.
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.
The ASQ dataset is organised hierarchically under the traces/ directory:
traces/
βββ dataset/
βββ retriever_config/
βββ agent_family/
βββ model/
βββ answers.tsv
βββ iter_queries.tsv
βββ retrieved_docs.tsv
βββ thoughts.tsv
Placeholders:
Each collection of traces comprises four TSV artifact files, with rows associated with a qid of the organic query:
| Artifact | Description |
|---|---|
answers.tsv | Answers generated by the agent for each query. |
iter_queries.tsv | Synthetic queries generated by the agent per query and iteration. |
retrieved_docs.tsv | Ranked list of documents retrieved per query and iteration. |
thoughts.tsv | CoT reasoning "thoughts" of the agent per query and iteration. |
Columns:
qid (string): qid of the organic query.answer (string): answer generated by the agent.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.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.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.Please see our GitHub repository:github.com/fpezzuti/ASQ.
The ASQ dataset is released under the MIT License. Individual source datasets may have their own licenses.
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.
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},
}
3 commits