---
title: "L2Dir — paper by Zong et al. (2026) — summary and ideas"
description: "They propose L2Dir, a plug-in framework with two parts: InfoTN , a contrastive loss built on a bounded Tensor Norm Similarity… 77 references."
authors: ["Tianyu Zong", "Rui Dai", "Hongzhu Yi", "Yuanxiang Wang", "Zhenghao Zhang", "Zhenyu Guan", "Yujia Yang", "Bingkang Shi", "Yueyang Ding", "Xiangxiang Chu", "Kaikui Liu", "Jungang Xu"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1604/"
doi: "10.18653/v1/2026.acl-long.1604"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zong-2026-l2dir-integrating-l-2-norm/"
---
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# L2Dir: Integrating L2-Norm and Directional Alignment for Unsupervised Contrastive Representation Learning in Multimodal Retrieval

**Authors:** [Tianyu Zong](https://md.research.elamur.ai/free/en/authors/tianyu-zong/), Rui Dai, [Hongzhu Yi](https://md.research.elamur.ai/free/en/authors/hongzhu-yi/), [Yuanxiang Wang](https://md.research.elamur.ai/free/en/authors/yuanxiang-wang/), Zhenghao Zhang, [Zhenyu Guan](https://md.research.elamur.ai/free/en/authors/zhenyu-guan/), Yujia Yang, [Bingkang Shi](https://md.research.elamur.ai/free/en/authors/bingkang-shi/), Yueyang Ding, [Xiangxiang Chu](https://md.research.elamur.ai/free/en/authors/xiangxiang-chu/), Kaikui Liu and Jungang Xu · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1604/) · [PDF](https://aclanthology.org/2026.acl-long.1604.pdf) · [DOI 10.18653/v1/2026.acl-long.1604](https://doi.org/10.18653/v1/2026.acl-long.1604) · [OpenAlex](https://openalex.org/W7166799554)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Contrastive training for multimodal retrieval usually relies on InfoNCE, which compares embeddings only by direction on the unit hypersphere and ignores vector length (L2-norm). The authors argue that magnitude carries useful signal. They show that an earlier text-only norm objective from TNCSE becomes unstable for vision-language models, because Qwen2-VL places queries and targets in two widely separated clusters.

They propose L2Dir, a plug-in framework with two parts:
- **InfoTN**, a contrastive loss built on a bounded Tensor Norm Similarity that couples the norm ratio with cosine similarity.
- **A small MLP head**, which reconstructs magnitudes from backbone features.

InfoTN is trained together with InfoNCE and needs no extra data, distillation or outside supervision. Across 95 tasks in the VLM2Vec-V2 and UniIR frameworks, L2Dir improves on the baselines for every backbone and scale tested. This suggests that modeling magnitude explicitly is a cheap, general way to strengthen multimodal embeddings.

## Key points
- The evaluation covers 81 tasks in VLM2Vec-V2 (MMEB-V2) with Qwen2-VL 2B/7B and GME 2B, plus 14 image-text tasks in UniIR with CLIP-B/L. Code is published on GitHub (tianyuzong/L2Dir_ACL2026).
- The similarity sim_TN is defined as 1 − L_TN and is shown to be bounded in [0, 1]. The authors also analyze its gradient behavior to justify why training is stable.
- The weight λ that combines InfoTN and InfoNCE was tuned on Qwen2-VL-2B. The best value, 0.5, was then used in all settings.
- On 12 image-text tasks (Hit@1), L2Dir reaches 57.32 against a baseline of 56.65. A plain unit-norm regularizer instead lowers the score to 55.60.
- On 50K Wikipedia texts, embedding norms trained with InfoTN track linguistic measures more closely than the baseline:

| Measure | Baseline r | With InfoTN r |
|---|---|---|
| Word entropy | 0.069 | 0.216 |
| Char entropy | 0.191 | 0.328 |

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [Alibaba Group](https://md.research.elamur.ai/free/en/companies/alibaba-group/) — Company, Hangzhou, China · 201 works · 17,173 citations
- [University of Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/universities/university-of-chinese-academy-of-sciences/) — University, Beijing, China · 188 works · 8,743 citations

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## Cite

Cite as: Tianyu Zong, Rui Dai, Hongzhu Yi, Yuanxiang Wang, Zhenghao Zhang, Zhenyu Guan, Yujia Yang, Bingkang Shi, Yueyang Ding, Xiangxiang Chu, Kaikui Liu and Jungang Xu. 2026. L2Dir: Integrating L2-Norm and Directional Alignment for Unsupervised Contrastive Representation Learning in Multimodal Retrieval. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zong2026l2dir,
  title = {{L2Dir: Integrating L2-Norm and Directional Alignment for Unsupervised Contrastive Representation Learning in Multimodal Retrieval}},
  author = {Tianyu Zong, Rui Dai, Hongzhu Yi, Yuanxiang Wang, Zhenghao Zhang, Zhenyu Guan, Yujia Yang, Bingkang Shi, Yueyang Ding, Xiangxiang Chu, Kaikui Liu and Jungang Xu},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1604},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1604}
}
```

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*Updated 2 November 2026*