---
title: "Lang2Act — paper by Xiong et al. (2026) — summary and ideas"
description: "Lang2Act replaces such operations with \"linguistic tools\": named textual actions (for example read_text_element ) that the… 32 references."
authors: ["Yuqi Xiong", "Chunyi Peng", "Zhipeng Xu", "Zhenghao Liu", "Zulong Chen", "Yukun Yan", "Shuo Wang", "Yu Gu", "Ge Yu"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.409/"
doi: "10.18653/v1/2026.findings-acl.409"
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/xiong-2026-lang2act-fine-grained-visual-reasoning/"
---
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# Lang2Act: Fine-Grained Visual Reasoning through Self-Emergent Linguistic Toolchains

**Authors:** Yuqi Xiong, Chunyi Peng, [Zhipeng Xu](https://md.research.elamur.ai/free/en/authors/zhipeng-xu/), [Zhenghao Liu](https://md.research.elamur.ai/free/en/authors/zhenghao-liu/), [Zulong Chen](https://md.research.elamur.ai/free/en/authors/zulong-chen/), [Yukun Yan](https://md.research.elamur.ai/free/en/authors/yukun-yan/), [Shuo Wang](https://md.research.elamur.ai/free/en/authors/shuo-wang/), [Yu Gu](https://md.research.elamur.ai/free/en/authors/yu-gu/) and [Ge Yu](https://md.research.elamur.ai/free/en/authors/ge-yu/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/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.findings-acl.409/) · [PDF](https://aclanthology.org/2026.findings-acl.409.pdf) · [DOI 10.18653/v1/2026.findings-acl.409](https://doi.org/10.18653/v1/2026.findings-acl.409) · [OpenAlex](https://openalex.org/W7166884159)

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

## Summary
Lang2Act addresses a weakness of Visual Retrieval-Augmented Generation (VRAG). Many systems sharpen a VLM's view of retrieved document pages with external image operations such as cropping or zooming. These operations separate seeing from reasoning, and a bad crop can permanently remove the evidence needed for the answer. Lang2Act replaces such operations with "linguistic tools": named textual actions (for example `read_text_element`) that the model generates as part of its reasoning, steering its attention toward the relevant page regions.

Training has two RL stages, both using DAPO:
1. **Exploration.** The VLM learns, through self-exploration rewarded by answer correctness, which visual actions help solve questions. Actions from successful trajectories are abstracted into a reusable toolbox.
2. **Exploitation.** The model is optimized to use that toolbox for downstream reasoning.

On several visual QA benchmarks, Lang2Act beats all baselines by more than 4%. Attention analyses show it locates ground-truth regions more often and relies more on visual evidence than on memorized knowledge. This suggests that perception can be strengthened without lossy pixel-level tools.

## Key points
- **Tool curation:** frequent self-emergent actions are collected into a tool pool. Each tool is stored as a triplet of name, description and parameter specification. Examples include `read_text_element` and `read_numeric_value`.
- **Backbones:** Qwen2.5-VL-3B, Qwen2.5-VL-7B and Qwen2.5-VL-32B. Tool-enhanced baselines include Pixel-Reasoner and VRAG-RL.
- **Prompting-only result (before any training), 7B backbone:** the Lang2Act tool format reaches an average of 56.19, ahead of Structured CoT (54.43) and Vanilla prompting (53.12).
- **SlideVQA case study:** VRAG-RL cropped away the key value "43%" and answered "56%". Lang2Act focused on the correct region and answered correctly.
- **Resources:** code and data are released at github.com/NEUIR/Lang2Act. The authors acknowledge that the causal link between attention shifts and answer correctness is not established; their evidence is correlational.

*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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## Cite

Cite as: Yuqi Xiong, Chunyi Peng, Zhipeng Xu, Zhenghao Liu, Zulong Chen, Yukun Yan, Shuo Wang, Yu Gu and Ge Yu. 2026. Lang2Act: Fine-Grained Visual Reasoning through Self-Emergent Linguistic Toolchains. Findings of the Association for Computational Linguistics: ACL 2026.

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

```bibtex
@inproceedings{xiong2026lang2act,
  title = {{Lang2Act: Fine-Grained Visual Reasoning through Self-Emergent Linguistic Toolchains}},
  author = {Yuqi Xiong, Chunyi Peng, Zhipeng Xu, Zhenghao Liu, Zulong Chen, Yukun Yan, Shuo Wang, Yu Gu and Ge Yu},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.409},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.409}
}
```

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- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/38/)

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*Updated 2 November 2026*