Tencent-Hunyuan/Simple-Attention-Sparsification
Code for our resarch paper "SAS: Simple Attention Sparsification via End-to-End Optimization of Context Ranking"
About the project
SAS is a gated sparse-attention mechanism that learns per-query which context blocks to attend to, optimizing context ranking end-to-end with the language modeling loss. The repo provides the paper's training and evaluation code.
Useful for
- Train sparse attention on Qwen3-4B/8B/14B with the OpenR1-Math-220k dataset
- Evaluate the model on MATH, GPQA-Diamond, AIME24/25 and LongBench-E via sglang-blocksparse
- Run BFCL function-calling and VitaBench tool-use benchmarks with sparse attention
README summarized by DeepSeek V4.1 Flash. Details may be inaccurate.
Why it’s trending
- 1 star so far today, about 1 expected by the end of the day.
- The repository is 8 days old and already has 59 stars.
Stars per day
Bars are daily stars, the line is the usual pace. Red marks spike days.
Numbers
- Total stars
- 59
- Stars in a day
- 1
- Forks
- 1
- Issues and pull requests
- 0
- Watchers
- 0
- Language
- Python
- Created
- September 10, 2026
- Last push
- September 14, 2026
Star trust
Growth looks organic: forks and discussion are in line with active projects, and stars arrive unevenly, the way people give them.
These are heuristics, not a verdict: we judge by the repository’s behavior, not by a list of stargazers.
Spotted in
- September 16, 2026Top new repositories this week: #184
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