---
license: mit
pipeline_tag: text-generation
---

# KV Admission: Learning What to Write for Efficient Long-Context Inference

This repository contains the official weights for the **Write-Gate MLP** introduced in the paper [KV Admission: Learning What to Write for Efficient Long-Context Inference](https://huggingface.co/papers/2512.17452).

## Abstract

Long-context LLM inference is bottlenecked by the quadratic attention complexity and linear KV cache growth. Prior approaches (KV Selection or Eviction) mitigate this post-hoc, but overlook the root inefficiency: indiscriminate writing to memory. We propose **Write-Gated KV (WG-KV)** to introduce a missing primitive: **KV Admission**. Instead of blindly persisting every token, WG-KV employs a lightweight, learnable mechanism to predict token utility before cache entry. By filtering out low-utility states early to maintain a compact global cache alongside a sliding local cache, WG-KV reduces memory usage by 46-68% and delivers significant prefill and decode speedups, while maintaining compatibility with FlashAttention and Paged-KV systems.

## Resources

- **Paper:** [KV Admission: Learning What to Write for Efficient Long-Context Inference](https://huggingface.co/papers/2512.17452)
- **GitHub Repository:** [EMCLab-Sinica/WG-KV](https://github.com/EMCLab-Sinica/WG-KV)

## Usage

Using these checkpoints requires the environment and custom implementations provided in the official repository. This includes a modified version of the Transformers library and a custom vLLM fork for sparse prefill kernels. Please refer to the [official installation guide](https://github.com/EMCLab-Sinica/WG-KV#%EF%B8%8F-installation).

After setting up the environment, you can run inference using the trained gate by specifying the checkpoint path:

```bash
python scripts/inference.py \
  --model_name meta-llama/Llama-3.1-8B-Instruct \
  --filtering_path weights/llama-3.1-8b-instruct-0.04.pt
```

The checkpoints follow the naming convention `{model_name}-{lambda}.pt`, where `lambda` (λ) controls the trade-off between sparsity and accuracy.

## Citation

```bibtex
@misc{wgkv,
   title={KV Admission: Learning What to Write for Efficient Long-Context Inference},
   author={Yen-Chieh Huang and Pi-Cheng Hsiu and Rui Fang and Ming-Syan Chen},
   year={2025},
   eprint={2512.17452},
   archivePrefix={arXiv},
   primaryClass={cs.LG},
   url={https://arxiv.org/abs/2512.17452}, 
}
```