{
  "annotation_id": "f1eb78aa-30db-467c-b2a2-4131b927dbee",
  "date_created": "2026-02-17T05:53:20.117000Z",
  "date_modified": "2026-02-17T05:53:20.117000Z",
  "file_hash": "be8b81b330bc4c9772e208870f639dcf3a4838636b3b325e7aac819f9448ef61",
  "private": false,
  "record": {
    "abstract": "We propose Quamba-SE, a soft-edge quantizer for State Space Model (SSM) activation quantization. Unlike existing methods, using standard INT8 operation, Quamba-SE employs three adaptive scales: high-precision for small values, standard scale for normal values, and low-precision for outliers. This preserves outlier information instead of hard clipping, while maintaining precision for other values. We evaluate on Mamba- 130M across 6 zero-shot benchmarks. Results show that Quamba- SE consistently outperforms Quamba, achieving up to +2.68% on individual benchmarks and up to +0.83% improvement in the average accuracy of 6 datasets.",
    "arxiv_id": "2601.09451",
    "authors": [
      "Yizhi Chen",
      "Ahmed Hemani"
    ],
    "categories": [
      "cs.LG",
      "cs.AI",
      "cs.AR"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Late Breaking Results: Quamba-SE: Soft-edge Quantizer for Activations in State Space Models",
    "url": "https://arxiv.org/abs/2601.09451",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "7ec69c77-213b-4328-b93b-49f71865834d",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}