README.md

---
language:
- en
license: apache-2.0
tags:
- retrieval
- reasoning
- embedding
- BRIGHT
- information-retrieval
library_name: transformers
pipeline_tag: feature-extraction
base_model: Qwen/Qwen3-4B-Instruct-2507
datasets:
- xlangai/BRIGHT
model-index:
- name: MRE-T1
  results:
  - task:
      type: Retrieval
    dataset:
      type: xlangai/BRIGHT
      name: BRIGHT (Short)
    metrics:
    - type: ndcg_at_10
      value: 39.6
      name: nDCG@10
  - task:
      type: Retrieval
    dataset:
      type: xlangai/BRIGHT
      name: BRIGHT (Long)
    metrics:
    - type: ndcg_at_10
      value: 35.1
      name: nDCG@10
---

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---

# MRE-T1: Mira Reasoning Embedding — Thought v1

**MRE-T1** (Mira Reasoning Embedding, Thought v1) is the first generation of our reasoning-intensive retrieval model series. The "Thought" in T1 reflects the model's core capability — it thinks before it retrieves, generating explicit reasoning chains to deeply understand query intent before producing embeddings.

MRE-T1 achieves state-of-the-art single-model performance on the [BRIGHT benchmark](https://brightbenchmark.github.io/), which evaluates retrieval models on tasks requiring complex reasoning capabilities.

## Highlights

- **BRIGHT Short nDCG@10: 39.6** — achieves the best single-model result on the short document retrieval leaderboard
- **BRIGHT Long nDCG@10: 35.1** — achieves the best single-model result on the long document retrieval leaderboard
- **Efficient**: Based on Qwen3-4B architecture, significantly smaller than many competing 7-8B models
- **Reasoning-aware**: Uses task-specific reasoning prompts with a special `<emb_token>` for embedding extraction

## Model Details

| Property | Value |
|----------|-------|
| Architecture | Qwen3ForCausalLM |
| Parameters | ~4B |
| Hidden Size | 2560 |
| Layers | 36 |
| Attention Heads | 32 (KV heads: 8) |
| Max Position | 262,144 |
| Precision | bfloat16 |
| Vocabulary | 151,670 |

## BRIGHT Benchmark Results

### Short Document Retrieval (nDCG@10)

| Task | MRE-T1 |
|------|--------|
| Biology | 55.3 |
| Earth Science | 56.5 |
| Economics | 32.9 |
| Psychology | 48.2 |
| Robotics | 33.1 |
| StackOverflow | 34.2 |
| Sustainable Living | 37.3 |
| LeetCode | 35.0 |
| Pony | 35.5 |
| AOPS | 16.7 |
| TheoremQA (Questions) | 43.3 |
| TheoremQA (Theorems) | 46.9 |
| **Average** | **39.6** |

### Long Document Retrieval (nDCG@10)

| Task | MRE-T1 |
|------|--------|
| Biology | 46.5 |
| Earth Science | 46 |
| Economics | 34.5 |
| Psychology | 52.7 |
| Robotics | 27.7 |
| StackOverflow | 22.2 |
| Sustainable Living | 45.2 |
| Pony | 6.3 |
| **Average** | **35.1** |

### Comparison with Other Models (Short, Single Model Only)

| Model | Size | BRIGHT Short nDCG@10 |
|-------|------|---------------------|
| **MRE-T1** | **~4B** | **39.6** |
| BGE-Reasoner-Embed-0928 | 8B | 38.1 |
| Seed1.5-Embedding | MoE | 27.2 |
| gte-Qwen1.5-7B-instruct | 7B | 22.5 |
| GritLM-7B | 7B | 21.0 |
| instructor-xl | 1.5B | 18.9 |
| SFR-Embedding-Mistral | 7B | 18.3 |
| e5-mistral-7b-instruct | 7B | 17.9 |

### Comparison with Other Models (Long, Single Model Only)

| Model | Size | BRIGHT Long nDCG@10 |
|-------|------|---------------------|
| **MRE-T1** | **~4B** | **35.1** |
| Google-Gecko-Text-Embedding-004 | — | 33.2 |
| gte-Qwen1.5-7B-instruct | 7B | 27.8 |
| SFR-Embedding-Mistral | 7B | 26.0 |
| e5-mistral-7b-instruct | 7B | 25.5 |
| voyage-large-2-instruct | — | 24.6 |
| Cohere-embed-english-v3.0 | — | 18.4 |
| bge-large-en-v1.5 | 335M | 14.8 |

## Usage

MRE-T1 uses task-specific system prompts for reasoning-enhanced retrieval. Each query is processed with a domain-specific instruction, and the model generates a reasoning chain followed by a special `<emb_token>` whose representation is used as the query embedding.

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ForwardAILabs/MRE-T1"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")

# Task-specific system prompts
TASK_PROMPTS = {
    "biology": "Given a Biology post, extract and briefly describe the core underlying principle or mechanism of this biology question. You MUST end every response with <emb_token>.",
    "earth_science": "Given an Earth Science post, identify the type of Earth science question and briefly describe the core principle for solving it. You MUST end every response with <emb_token>.",
    "economics": "Given an Economics post, analyze the user's core blind spot and the applicable economic analysis framework. You MUST end every response with <emb_token>.",
    "psychology": "Given a Psychology post, extract the user's blind spot and key psychological concepts. You MUST end every response with <emb_token>.",
    "robotics": "Given a Robotics post, diagnose the core issue within the robotics environment and error logs, and point out the applicable technical principles. You MUST end every response with <emb_token>.",
    "stackoverflow": "Given a Stack Overflow post, extract the core underlying technical principle for solving the code error. You MUST end every response with <emb_token>.",
    "sustainable_living": "Given a Sustainable Living post, identify the key scientific concepts and background knowledge required for a closed-loop solution to the life phenomenon or practice. You MUST end every response with <emb_token>.",
    "leetcode": "Given a Coding problem, extract the core algorithm principle (or data structure) and general problem-solving approach. You MUST end every response with <emb_token>.",
    "pony": "Given a Pony question, locate the core knowledge points from the Pony language official documentation needed to solve the code completion problem. You MUST end every response with <emb_token>.",
    "aops": "Given a Math problem, analyze the problem type characteristics and core examination principles of this math competition problem. You MUST end every response with <emb_token>.",
    "theoremqa_questions": "Given a Math problem, analyze the problem type characteristics and core examination principles of this math competition problem. You MUST end every response with <emb_token>.",
    "theoremqa_theorems": "Given a Math problem, distill the core mathematical principles and problem-solving techniques required for the real-world scenario. You MUST end every response with <emb_token>.",
}

# Example: Generate reasoning-enhanced query embedding
task = "stackoverflow"
query = "How to fix a segmentation fault when using shared_ptr in a multithreaded C++ application?"

messages = [
    {"role": "system", "content": TASK_PROMPTS[task]},
    {"role": "user", "content": query}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model(**inputs, output_hidden_states=True)
    # Use the last hidden state at the <emb_token> position as the embedding
    embedding = outputs.hidden_states[-1][0, -1, :]

print(f"Embedding shape: {embedding.shape}")
```

## Training

MRE-T1 is trained using a two-stage approach on the Qwen3-4B base model:
1. **Stage 1**: Supervised fine-tuning with task-specific reasoning prompts
2. **Stage 2**: Reinforcement learning to optimize retrieval quality

Training data is curated from diverse reasoning-intensive domains including mathematics, science, programming, and social sciences.

## Evaluation

Evaluated on [BRIGHT](https://brightbenchmark.github.io/) (Bridging Reasoning and Information Gathering with Holistic Thinking), a benchmark specifically designed to test retrieval models on tasks requiring complex reasoning.

## Citation

If you use MRE-T1 in your research, please cite:

```bibtex
@misc{mre-t1-2026,
  title={MRE-T1: Reasoning-Enhanced Retrieval Model},
  author={Forward AI},
  year={2026},
  url={https://huggingface.co/ForwardAILabs/MRE-T1}
}
```

## License

Apache 2.0

---

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