---
title: "GenesisFunc — paper by Xu et al. (2026) — summary and ideas"
description: "Training LLMs to call functions reliably requires large amounts of varied, accurate tool-use data. 40 references."
authors: ["Hao-Xiang Xu", "Chong Deng", "Jiaqing Liu", "Wen Wang", "Qian Chen", "Lujia Bao", "Xiangang Li", "Zhen-Hua Ling"]
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.1319/"
doi: "10.18653/v1/2026.acl-long.1319"
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/xu-2026-genesisfunc-multi-agent-data-generation/"
---
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# GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling

**Authors:** [Hao-Xiang Xu](https://md.research.elamur.ai/free/en/authors/hao-xiang-xu/), [Chong Deng](https://md.research.elamur.ai/free/en/authors/chong-deng/), [Jiaqing Liu](https://md.research.elamur.ai/free/en/authors/jiaqing-liu/), [Wen Wang](https://md.research.elamur.ai/free/en/authors/wen-wang/), [Qian Chen](https://md.research.elamur.ai/free/en/authors/qian-chen/), Lujia Bao, [Xiangang Li](https://md.research.elamur.ai/free/en/authors/xiangang-li/) and [Zhen-Hua Ling](https://md.research.elamur.ai/free/en/authors/zhen-hua-ling/) · **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.1319/) · [PDF](https://aclanthology.org/2026.acl-long.1319.pdf) · [DOI 10.18653/v1/2026.acl-long.1319](https://doi.org/10.18653/v1/2026.acl-long.1319) · [OpenAlex](https://openalex.org/W7166850375)

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

## Summary

Training LLMs to call functions reliably requires large amounts of varied, accurate tool-use data. Real data is expensive to collect and annotate, and existing synthetic pipelines tend to rely on unstable APIs, scale poorly to new tools and lack diversity and quality control. GenesisFunc is an automated, three-stage pipeline that addresses these problems.

1. It draws a curated pool of dependable tools from the BFCL benchmark.
2. A multi-agent system helps a dialogue generator produce conversations covering single-turn, multi-turn and special cases. It uses history-aware role assignment and parameter-slot selection for diversity, and agent-based scoring for quality.
3. A multi-stage evaluation combines rule-based checks, model-based checks and targeted human review.

The authors fine-tuned Qwen3-8B on the resulting data to obtain GenesisFunc-8B. It beats open-source models of similar size on in-domain BFCL and on out-of-domain API-Bank and ACEBench, and is competitive with some API-based models. The pipeline also extends to new tool sets without hurting other benchmarks. The code and dataset are publicly released, which makes the approach a practical, reproducible recipe for building tool-using models.

## Key points

- **Tool Pool:** 1,000 tools taken from the BFCL evaluation set. GPT-4o clustered the tools semantically to remove near-duplicates, followed by light manual verification.
- **Overall accuracy (Table 4):** GenesisFunc-8B scores 88.55 on BFCL, 64.79 on API-Bank and 78.64 on ACEBench. This compares with 83.20 / 55.56 / 70.79 for base Qwen3-8B and 85.89 / 56.21 / 35.65 for ToolACE-8B.
- **Scalability:**
  - Adding ACEBench tools to the pool raises ACEBench accuracy from 78.64 to 81.87.
  - BFCL stays roughly stable (87.89) and API-Bank stays roughly stable (65.11).
  - The appendices report the same pattern across model sizes and backbones.
- **Reinforcement learning:**
  - Setup: GRPO with rewards for format compliance and functional correctness, using Qwen3-8B's thinking mode.
  - The hybrid variant (SFT on single-turn and special-case data, then RL on multi-turn dialogues) raises multi-turn accuracy on ACEBench from 65.00 to 70.00.
  - Its Normal score rises from 73.60 to 75.20, while Special stays near the SFT level (82.88 vs. 83.67).
- **Limitations noted by the authors:** the model still trails GPT-4-class systems in general reasoning, and the data does not yet cover highly complex, tightly chained multi-turn tool sequences.
- **Availability:** the pipeline and dataset are at https://github.com/famoustourist/GenesisFunc.

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

## Institutions

- [University of Science and Technology of China](https://md.research.elamur.ai/free/en/universities/university-of-science-and-technology-of-china/) — University, Hefei, China · 145 works · 114,409 citations

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## Cite

Cite as: Hao-Xiang Xu, Chong Deng, Jiaqing Liu, Wen Wang, Qian Chen, Lujia Bao, Xiangang Li and Zhen-Hua Ling. 2026. GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling. 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{xu2026genesisfunc,
  title = {{GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling}},
  author = {Hao-Xiang Xu, Chong Deng, Jiaqing Liu, Wen Wang, Qian Chen, Lujia Bao, Xiangang Li and Zhen-Hua Ling},
  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.1319},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1319}
}
```

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*Updated 2 November 2026*