Tevatron/AgentIR-indexes

This dataset hosts the AgentIR-4B indexes.

• Paper: AgentIR: Reasoning-Aware Retrieval for Deep Research Agents
• Code: https://github.com/texttron/AgentIR
• Model: Tevatron/AgentIR-4B
• Project Page: https://texttron.github.io/AgentIR/

For usage details of this index, please see 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:

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

@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}
}