---
license: mit
datasets:
- EverMind-AI/MSA-RAG-BENCHMARKS
language:
- en
base_model:
- Qwen/Qwen3-4B-Instruct-2507
pipeline_tag: question-answering
---
# MSA-4B
[](https://github.com/EverMind-AI)
## Highlights
Long-term memory is essential for general intelligence, yet the **full attention** bottleneck constrains most LLMs' **effective context length** to **128K–1M**. Existing attempts，hybrid linear attention, fixed-size state memory (e.g., RNNs), and external storage like **RAG/agents**，either suffer rapid precision decay and latency growth at extreme scales, lack end-to-end differentiability or dynamic memory maintenance, or require complex pipelines. We present **Memory Sparse Attention (MSA)**: an **end-to-end trainable, scalable sparse** **latent-state memory** framework. Core ideas include:
- **Scalable sparse attention** + **document-wise RoPE** (parallel/global) achieving **near-linear complexity** in both training and inference;
- **KV cache compression** with a **Memory Parallel** inference engine to deliver **100M token** throughput on **2×A800** GPUs;
- **Memory Interleave** for multi-round, multi-hop reasoning across scattered memory segments.
On long-context QA and NIAH (Needle-in-a-Haystack) benchmarks, **MSA** surpasses same-backbone RAG, best-of-breed RAG stacks, and leading long-context models. Across an unprecedented **16K→100M token** range, MSA shows **< 9%** degradation, suggesting a practical path to **decouple memory capacity from reasoning**.
> **Scaling from 16K→100M tokens**: MSA fuses top-k selection with sparse attention to remain end-to-end differentiable while allowing document decoupling at inference. On MS MARCO, MSA sustains **<9%** degradation and exhibits strong extrapolation.
> *Some baseline curves end early due to their context limits.*
*Figure 1: MSA scalability under extreme-long contexts*
## Model Overview
This model is based on **Qwen3-4B-Instruct-2507** with **Memory Sparse Attention (MSA)**.
- Number of Parameters: 4.0B
- Number of Layers: 36
- Number of MSA Layers: 18
- Number of Attention Heads (GQA): 32 for Q and 8 for KV
- Based on Qwen/Qwen3-4B-Instruct-2507
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [**paper**](https://arxiv.org/abs/2603.23516) and [**GitHub**](https://github.com/EverMind-AI/MSA).
## Performance
> **Setup**
> **QA**: 9 datasets (MS MARCO v1, NQ, DuReader, TriviaQA(10M), NarrativeQA, PopQA, 2WikiMultiHopQA, HotpotQA, MuSiQue), memory banks **277K→10M tokens**, metric: **LLM judge (0–5)**.
> **NIAH (RULER)**: 8 subtasks, **32K→1M tokens**, report average accuracy.
> **Backbone**: Qwen3‑4B‑Instruct‑2507. Compare to same-backbone RAG and best-of-breed RAG stacks (KaLMv2 + large generators, optional reranker).
### Table 1: MSA vs same-backbone RAG (Qwen3‑4B)
**Summary**: Average **3.760**, improving over standard RAG (**+16.0%**), RAG+rerank (**+11.5%**), and HippoRAG2 (**+14.8%**) using their best@k; MSA leads on all but NarrativeQA within the same-backbone group.
| Dataset | Tokens | Qwen3-4B R@1 | R@5 | R@10 | Qwen3-4B (RR) R@1 | R@5 | R@10 | HippoRAG2 R@1 | R@5 | R@10 | MSA (adaptive) |
|---------|--------|--------------|------|-------|----------------------|------|-------|------------------|------|--------|----------------|
| MS MARCO v1 | 7.34M | 2.893 | 3.011 | 3.005 | 2.934 | 3.032 | 3.017 | 2.676 | 3.005 | 3.019 | **4.141** |
| Natural Questions | 1.47M | 3.452 | 3.374 | 3.297 | 3.494 | 3.408 | 3.385 | 3.338 | 3.389 | 3.374 | **3.545** |
| DuReader | 277K | 3.726 | 3.579 | 3.594 | 3.848 | 3.618 | 3.607 | 2.941 | 3.485 | 3.415 | **4.155** |
| TriviaQA (10M) | 10M | 4.133 | 4.414 | 4.273 | 4.313 | 4.375 | 4.391 | 4.188 | 4.430 | 4.367 | **4.621** |
| NarrativeQA | 538K | 1.611 | 2.567 | 2.860 | **3.638** | 3.492 | 3.536 | 1.959 | 2.628 | 2.655 | 3.395 |
| PopQA | 1.18M | 2.959 | 3.273 | 3.299 | 3.315 | 3.264 | 3.266 | 3.111 | 3.249 | 3.249 | **3.433** |
| 2WikiMultiHopQA | 722K | 1.065 | 3.055 | 3.136 | 1.187 | 3.057 | 3.159 | 1.045 | 3.180 | 3.330 | **4.280** |
| HotpotQA | 1.35M | 2.252 | 3.582 | 3.787 | 2.642 | 3.990 | 4.022 | 3.230 | 3.770 | 3.970 | **4.061** |
| MuSiQue | 1.41M | 0.936 | 1.752 | 1.928 | 1.144 | 1.960 | 1.965 | 1.020 | 1.907 | 2.095 | **2.211** |
| **Average** | — | 2.559 | 3.179 | 3.242 | 2.946 | 3.355 | 3.372 | 2.612 | 3.227 | 3.275 | **3.760** |
*Table 1: Same-backbone RAG vs MSA (@1/@5/@10 vs MSA @adaptive)*
### Table 2: MSA vs best-of-breed RAG (large backbones)
**Summary**: Against **KaLMv2+Qwen3‑235B** and **KaLMv2+Llama‑3.3‑70B** (w/ and w/o reranking), MSA achieves the best score on **4/9** datasets and an average **3.760**, with relative gains of **+7.2%**, **+5.0%**, **+10.7%**, and **+5.4%** over the strongest configurations respectively. Gaps on a few datasets (e.g., MuSiQue) are largely attributable to parameter-count and intrinsic reasoning capacity.
| Dataset | KaLMv2 + Qwen3‑235B R@1 | R@5 | R@10 | Qwen3‑235B (RR) R@1 | R@5 | R@10 | KaLMv2 + Llama‑3.3 R@1 | R@5 | R@10 | Llama‑3.3 (RR) R@1 | R@5 | R@10 | MSA (adaptive) |
|---------|---------------------------|------|-------|--------------------------|------|-------|------------------------------|------|--------|-------------------------|------|--------|----------------|
| MS MARCO v1 | 2.846 | 3.028 | 3.027 | 2.886 | 3.020 | 2.995 | 2.649 | 2.904 | 2.919 | 2.881 | 2.955 | 2.952 | **4.141** |
| Natural Questions | 3.711 | 3.670 | 3.694 | 3.621 | 3.610 | 3.645 | 3.675 | 3.674 | 3.662 | **3.756** | 3.665 | 3.647 | 3.545 |
| DuReader | 4.044 | 3.991 | 3.978 | 3.973 | 3.932 | 3.891 | 4.051 | 3.846 | 3.742 | 3.967 | 3.776 | 3.780 | **4.155** |
| TriviaQA (10M) | 4.367 | 4.656 | 4.578 | 4.492 | 4.320 | 4.555 | 4.273 | **4.740** | 4.719 | 4.547 | 4.703 | 4.695 | 4.621 |
| NarrativeQA | 1.413 | 2.130 | 2.427 | 3.212 | **3.427** | 3.375 | 1.290 | 2.123 | 2.382 | 3.150 | 3.263 | 3.317 | 3.395 |
| PopQA | 2.810 | 3.347 | 3.396 | 3.268 | 3.380 | 3.376 | 2.787 | 3.298 | 3.305 | 3.337 | 3.384 | 3.362 | **3.433** |
| 2WikiMultiHopQA | 2.646 | 3.579 | 3.582 | 1.855 | 3.381 | 3.583 | 1.339 | 3.263 | 3.445 | 1.651 | 3.332 | 3.541 | **4.280** |
| HotpotQA | 3.497 | 4.090 | **4.225** | 3.341 | 4.141 | 4.194 | 3.070 | 3.896 | 4.127 | 3.428 | 4.145 | 4.203 | 4.061 |
| MuSiQue | 1.988 | 2.462 | **2.647** | 1.801 | 2.522 | 2.605 | 1.704 | 2.317 | 2.258 | 1.895 | 2.462 | 2.614 | 2.211 |
| **Average** | 3.036 | 3.439 | 3.506 | 3.161 | 3.526 | 3.580 | 2.760 | 3.340 | 3.396 | 3.179 | 3.521 | 3.568 | **3.760** |
*Table 2: SOTA RAG stacks (strong retriever + large generator + optional reranker) vs MSA*
## Quick Start
**1. Download code from our [Github](https://github.com/EverMind-AI/MSA) and install dependencies**
```bash
git clone https://github.com/EverMind-AI/MSA
conda create -n msa python=3.12 -y
conda activate msa
pip install -r requirements.txt
pip install flash-attn==2.7.4.post1 --no-build-isolation
```
**2. Download model**
```bash
mkdir ckpt
pip install -U huggingface_hub
export HF_ENDPOINT=https://hf-mirror.com
huggingface-cli download --resume-download EverMind-AI/MSA-4B --local-dir ckpt/MSA-4B
```
**3. Download benchmarks**
Benchmark data is hosted on [EverMind-AI/MSA-RAG-BENCHMARKS](https://huggingface.co/datasets/EverMind-AI/MSA-RAG-BENCHMARKS) and will be automatically downloaded to `data/` on first run, based on the benchmarks specified in `scripts/run_benchmarks.sh`. No manual download is needed.
**4. Run inference on benchmarks**
```bash
bash scripts/run_benchmarks.sh eval_benchmark
```
**5. Compute LLM-based scores**
```bash
bash scripts/calculate_llm_score.sh eval_benchmark
```
## Supported Benchmarks
| Category | Benchmark |
|---|---|
| Multi-hop QA | `2wikimultihopqa`, `hotpotqa`, `musique` |
| Single-hop QA | `nature_questions`, `triviaqa_06M`, `triviaqa_10M`, `msmarco_v1`, `dureader`, `ms_100M`, `hipporag_narrative`, `hipporag_popqa` |
## Citation
If you find our work helpful, feel free to give us a cite.
```bibtex
@misc{chen2026msamemorysparseattention,
title={MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens},
author={Yu Chen and Runkai Chen and Sheng Yi and Xinda Zhao and Xiaohong Li and Jianjin Zhang and Jun Sun and Chuanrui Hu and Yunyun Han and Lidong Bing and Yafeng Deng and Tianqiao Chen},
year={2026},
eprint={2603.23516},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2603.23516},
}
```
## Acknowledgments
This model are maintained by the MSA authors. For project updates, please visit the **Homepage**: https://evermind.ai/