Add paper badges and arXiv article citation

README.md

@@ -1,86 +1,96 @@
----
-license: mit
-language:
-- en
-tags:
-- dami
-- linear-attention
-- long-context
-- initialization
-- ruler
----
-
-# DAMI: Dual-Axis Memory Initialization for Enhanced Long-Context Retrieval in Gated Linear Attention
-
-Zhangchi Feng · Harry Jiang · Yangyifei Luo · Ray Zhang · Richong Zhang
-
-[Project page](https://speedai.com/research/dami) · [Code, evaluation data and scripts](https://cnb.cool/speed-ai/research/DAMI)
-
-DAMI allocates memory timescales across heads and input responses across layers to improve long-context retrieval. This repository contains the 15 paper checkpoints: five architectures (GDN, KDA, GDN2, Mamba-2, RWKV-7) with three initialization conditions (Random, PoST, DAMI). Each model has approximately 100M parameters and was trained for approximately 5B tokens with a 4096-token training context.
-
-## Checkpoints
-
-All checkpoints are stored under `checkpoints/`, using the directory names below. Each directory contains the full model weights (`model.safetensors`), `config.json`, `generation_config.json`, `tokenizer.json`, and `tokenizer_config.json`.
-
-| Architecture | Random | PoST | DAMI |
-| --- | --- | --- | --- |
-| GDN | `main_gdn_random` | `main_gdn_PoST` | `main_gdn_DAMI` |
-| KDA | `main_kda_random` | `main_kda_PoST` | `main_kda_DAMI` |
-| GDN2 | `main_gdn2_random` | `main_gdn2_PoST` | `main_gdn2_DAMI` |
-| Mamba-2 | `main_mamba2_random` | `main_mamba2_PoST` | `main_mamba2_DAMI` |
-| RWKV-7 | `main_rwkv7_random` | `main_rwkv7_PoST` | `main_rwkv7_DAMI` |
-
-## Download and evaluation
-
-Clone the code repository and install its dependencies as described in its README. From the code repository root, download the checkpoints:
-
-```python
-from huggingface_hub import snapshot_download
-
-snapshot_download(
-    repo_id="SpeedAI-Lab/DAMI",
-    local_dir=".",
-    allow_patterns=["checkpoints/**"],
-)
-```
-
-To download only GDN + DAMI, use `allow_patterns=["checkpoints/main_gdn_DAMI/*"]`. Access to this repository requires authentication while it is private.
-
-Run all 15 checkpoints on the fixed RULER evaluation data in Linux/WSL with CUDA:
-
-```bash
-python evaluate_ruler.py --preflight --all --models-root checkpoints
-python evaluate_ruler.py --all --models-root checkpoints \
-  --output-dir outputs/ruler-retest --skip-done
-```
-
-For a single checkpoint, replace `--all` with `--architecture gdn --initialization dami`. The evaluator includes the architecture-specific FLA and RWKV adaptations required by these checkpoints; use the provided loading path for evaluation.
-
-The code repository includes 5,500 fixed samples across 11 RULER task/length settings and 82,500 original predictions. Evaluation uses greedy decoding with up to 128 new tokens. Scores use case-insensitive answer containment; the average weights all 11 settings equally.
-
-## RULER results
-
-Average accuracy (%), as reported in the paper:
-
-| Architecture | Random | PoST | DAMI |
-| --- | ---: | ---: | ---: |
-| GDN | 30.42 | 33.49 | 44.36 |
-| KDA | 26.38 | 33.67 | 43.09 |
-| GDN2 | 30.27 | 33.47 | 43.71 |
-| Mamba-2 | 9.95 | 13.73 | 24.42 |
-| RWKV-7 | 23.87 | 20.69 | 29.16 |
-
-## Citation
-
-```bibtex
-@misc{feng2026dami,
-  title={DAMI: Dual-Axis Memory Initialization for Enhanced Long-Context Retrieval in Gated Linear Attention},
-  author={Feng, Zhangchi and Jiang, Harry and Luo, Yangyifei and Zhang, Ray and Zhang, Richong},
-  year={2026},
-  url={https://speedai.com/research/dami}
-}
-```
-
-## License
-
-These checkpoints are released under the MIT License. See the LICENSE file. Benchmark attribution and data notices are provided in the code repository.
+---
+license: mit
+language:
+- en
+tags:
+- dami
+- linear-attention
+- long-context
+- initialization
+- ruler
+- arxiv:2610.41994
+---
+
+# DAMI: Dual-Axis Memory Initialization for Enhanced Long-Context Retrieval in Gated Linear Attention
+
+<div align="center">
+
+[![License: MIT](badges/license.svg)](https://huggingface.co/SpeedAI-Lab/DAMI/blob/main/LICENSE)
+[![arXiv: 2610.41994](badges/arxiv.svg)](https://arxiv.org/abs/2610.41994)
+![GitHub: link pending](badges/github.svg)
+[![Hugging Face: Checkpoints](badges/huggingface.svg)](https://huggingface.co/SpeedAI-Lab/DAMI)
+
+</div>
+
+Zhangchi Feng · Harry Jiang · Yangyifei Luo · Ray Zhang · Richong Zhang
+
+[Project page](https://speedai.com/research/dami) · [Code, evaluation data and scripts](https://cnb.cool/speed-ai/research/DAMI)
+
+DAMI allocates memory timescales across heads and input responses across layers to improve long-context retrieval. This repository contains the 15 paper checkpoints: five architectures (GDN, KDA, GDN2, Mamba-2, RWKV-7) with three initialization conditions (Random, PoST, DAMI). Each model has approximately 100M parameters and was trained for approximately 5B tokens with a 4096-token training context.
+
+## Checkpoints
+
+All checkpoints are stored under `checkpoints/`, using the directory names below. Each directory contains the full model weights (`model.safetensors`), `config.json`, `generation_config.json`, `tokenizer.json`, and `tokenizer_config.json`.
+
+| Architecture | Random | PoST | DAMI |
+| --- | --- | --- | --- |
+| GDN | `main_gdn_random` | `main_gdn_PoST` | `main_gdn_DAMI` |
+| KDA | `main_kda_random` | `main_kda_PoST` | `main_kda_DAMI` |
+| GDN2 | `main_gdn2_random` | `main_gdn2_PoST` | `main_gdn2_DAMI` |
+| Mamba-2 | `main_mamba2_random` | `main_mamba2_PoST` | `main_mamba2_DAMI` |
+| RWKV-7 | `main_rwkv7_random` | `main_rwkv7_PoST` | `main_rwkv7_DAMI` |
+
+## Download and evaluation
+
+Clone the code repository and install its dependencies as described in its README. From the code repository root, download the checkpoints:
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="SpeedAI-Lab/DAMI",
+    local_dir=".",
+    allow_patterns=["checkpoints/**"],
+)
+```
+
+To download only GDN + DAMI, use `allow_patterns=["checkpoints/main_gdn_DAMI/*"]`. Access to this repository requires authentication while it is private.
+
+Run all 15 checkpoints on the fixed RULER evaluation data in Linux/WSL with CUDA:
+
+```bash
+python evaluate_ruler.py --preflight --all --models-root checkpoints
+python evaluate_ruler.py --all --models-root checkpoints \
+  --output-dir outputs/ruler-retest --skip-done
+```
+
+For a single checkpoint, replace `--all` with `--architecture gdn --initialization dami`. The evaluator includes the architecture-specific FLA and RWKV adaptations required by these checkpoints; use the provided loading path for evaluation.
+
+The code repository includes 5,500 fixed samples across 11 RULER task/length settings and 82,500 original predictions. Evaluation uses greedy decoding with up to 128 new tokens. Scores use case-insensitive answer containment; the average weights all 11 settings equally.
+
+## RULER results
+
+Average accuracy (%), as reported in the paper:
+
+| Architecture | Random | PoST | DAMI |
+| --- | ---: | ---: | ---: |
+| GDN | 30.42 | 33.49 | 44.36 |
+| KDA | 26.38 | 33.67 | 43.09 |
+| GDN2 | 30.27 | 33.47 | 43.71 |
+| Mamba-2 | 9.95 | 13.73 | 24.42 |
+| RWKV-7 | 23.87 | 20.69 | 29.16 |
+
+## Citation
+
+```bibtex
+@article{feng2026dami,
+  title={DAMI: Dual-Axis Memory Initialization for Enhanced Long-Context Retrieval in Gated Linear Attention},
+  author={Feng, Zhangchi and Jiang, Harry and Luo, Yangyifei and Zhang, Ray and Zhang, Richong},
+  journal={arXiv preprint arXiv:2610.41994},
+  year={2026}
+}
+```
+
+## License
+
+These checkpoints are released under the MIT License. See the LICENSE file. Benchmark attribution and data notices are provided in the code repository.