| | --- |
| | task_categories: |
| | - text-retrieval |
| | tags: |
| | - agent |
| | --- |
| | |
| | This dataset hosts the [AgentIR-4B](https://huggingface.co/Tevatron/AgentIR-4B) indexes. |
| |
|
| | - **Paper:** [AgentIR: Reasoning-Aware Retrieval for Deep Research Agents](https://huggingface.co/papers/2603.04384) |
| | - **Code:** [https://github.com/texttron/AgentIR](https://github.com/texttron/AgentIR) |
| | - **Model:** [Tevatron/AgentIR-4B](https://huggingface.co/Tevatron/AgentIR-4B) |
| | - **Project Page:** [https://texttron.github.io/AgentIR/](https://texttron.github.io/AgentIR/) |
| |
|
| | For usage details of this index, please see [https://github.com/wu-ming233/AgentIR-dev/tree/main/evaluation](https://github.com/wu-ming233/AgentIR-dev/tree/main/evaluation). |
| |
|
| | ## Quick Usage |
| |
|
| | Below is the example code from the official repository to embed queries (including reasoning) and documents using the AgentIR-4B model: |
| |
|
| | ```python |
| | import torch |
| | from transformers import AutoModel, AutoTokenizer |
| | |
| | MODEL = "Tevatron/AgentIR-4B" |
| | PREFIX = "Instruct: Given a user's reasoning followed by a web search query, retrieve relevant passages that answer the query while incorporating the user's reasoning |
| | Query:" |
| | QUERY = """Reasoning: Search results show some relevant info about music and Grammy. We need a composer who won a Grammy, could be from Sweden/Finland/Austria (joined 1995)? The person is known for a certain creation that is a subgenre known for euphoric finale. Which subgenre has a euphoric finale? "Progressive house"? There's a structure: Build-up, breakdown, climax, drop, euphoria. They started creating this piece in a small studio's backroom. |
| | |
| | Query: "backroom" "studio" "early 2010s" "euphoric" |
| | """ |
| | DOCS = [ |
| | "35+ Studios With Upcoming Games to Watch: Turtle Rock Studios\n\nMaking its name on the classic Left 4 Dead series of games, Turtle Rock Studios is working on an all-new co-op game called Back 4 Blood that sees you fighting through a zombie apocalypse. Sound familiar? Announced in early 2019 and being published", |
| | "name: Otto Knows\nimage_upright: 1.25\nbirth_name: Otto Jettman\nbirth_date: 6 05 1989\nbirth_place: Stockholm, Sweden\ngenre: Electro house, house, progressive house\noccupation: DJ, music producer, remixer\n\nOtto Jettman (born 6 May 1989), better known by his stage name Otto Knows is a Swedish DJ, producer and remixer who has had a number of hits in Sweden, Belgium and the Netherlands" |
| | ] |
| | |
| | def embed(texts, model, tokenizer, device, is_query=False): |
| | batch = tokenizer( |
| | [PREFIX + t if is_query else t for t in texts], |
| | padding=True, |
| | truncation=True, |
| | max_length=8192, |
| | return_tensors="pt", |
| | ) |
| | batch = {k: v.to(device) for k, v in batch.items()} |
| | with torch.no_grad(): |
| | hidden = model(**batch, return_dict=True).last_hidden_state |
| | reps = hidden[:, -1] |
| | return torch.nn.functional.normalize(reps, p=2, dim=-1).cpu() |
| | |
| | model = AutoModel.from_pretrained(MODEL, torch_dtype=torch.float16, device_map="auto") |
| | device = model.device |
| | tokenizer = AutoTokenizer.from_pretrained(MODEL, padding_side="left") |
| | |
| | q = embed([QUERY], model, tokenizer, device, is_query=True)[0] |
| | docs = embed(DOCS, model, tokenizer, device) |
| | for doc, vec in zip(DOCS, docs): |
| | print(f"{torch.dot(q, vec).item():.6f} {doc}") |
| | ``` |
| |
|
| | ## Citation |
| | ```bibtex |
| | @article{chen2026AgentIR, |
| | title={AgentIR: Reasoning-Aware Retrieval for Deep Research Agents}, |
| | author={Zijian Chen and Xueguang Ma and Shengyao Zhuang and Jimmy Lin and Akari Asai and Victor Zhong}, |
| | year={2026}, |
| | journal={arXiv preprint arXiv:2603.04384} |
| | } |
| | ``` |
