---
title: "ProactiveEval — paper by Liu et al. (2026) — summary and ideas"
description: "Each study uses its own domain, data and metrics, so models are hard to compare. 54 references, cited by 3 papers here."
authors: ["Tianjian Liu", "Fanqi Wan", "Jiajian Guo", "Xiaojun Quan"]
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.1906/"
doi: "10.18653/v1/2026.acl-long.1906"
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/liu-2026-proactiveeval-unified-evaluation-framework-proactive/"
---
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# ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents

**Authors:** Tianjian Liu, [Fanqi Wan](https://md.research.elamur.ai/free/en/authors/fanqi-wan/), Jiajian Guo and [Xiaojun Quan](https://md.research.elamur.ai/free/en/authors/xiaojun-quan/) · **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.1906/) · [PDF](https://aclanthology.org/2026.acl-long.1906.pdf) · [DOI 10.18653/v1/2026.acl-long.1906](https://doi.org/10.18653/v1/2026.acl-long.1906) · [OpenAlex](https://openalex.org/W4414429882)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 3 in our library, of the 149,439 papers whose bibliographies we read

## Summary
Research on proactive dialogue agents, which start conversations and steer users toward goals without being asked, has been fragmented. Each study uses its own domain, data and metrics, so models are hard to compare. ProactiveEval addresses this with one shared framework that splits proactivity into two stages. In **target planning**, a model reads user information and trigger factors and derives a goal plus sub-targets. In **dialogue guidance**, it opens and leads a multi-turn conversation with a simulated user whose resistance is controlled by a Big Five agreeableness setting. Both stages are graded by an LLM judge on a 1–10 scale. The authors also built a pipeline that generates test environments automatically and makes them diverse and difficult. Using it, they assembled 328 environments and evaluated 22 LLMs. DeepSeek-R1 was strongest at planning and Claude-3.7-Sonnet at guidance. "Thinking" modes improved planning but did not measurably improve guidance. They reduced passive openings, yet sometimes made replies pushier or less natural. The benchmark gives a common, reproducible basis for measuring proactivity across domains.

## Key points
- **Scope:** 328 environments cover 6 domains: Recommendation, Persuasion, Ambiguous Instruction, Long-term Follow-up, System Operation and Glasses Assistant. One of these domains previously had no public benchmark. The models tested include GPT, Llama, Claude, DeepSeek, Gemini, Grok and Qwen variants.
- **Data synthesis:** The pipeline combines three techniques:
  - a hierarchical environment topic tree for diversity;
  - target ensembling to refine reference targets;
  - adversarial obfuscation rewriting and noise injection to raise difficulty.
- **Guidance metrics:** The judge scores effectiveness, personalization, tone, engagement and naturalness. Simulated users have low, medium or high agreeableness.
- **Judge reliability:** Weighted Kappa against human raters was 0.784 for planning and 0.730 for guidance. In the protocol ablation, planning agreement rose from 0.080 with a vanilla prompt to 0.784 with references, ICL examples and CoT.
- **Effect of targets:** Removing explicit targets lowered guidance scores by 25.80% for Qwen2.5-7B-Instruct and by about 10–12% for Claude-3.7-Sonnet, with or without thinking. Better IFEval scores were associated with better guidance.

*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

- [Sun Yat-sen University](https://md.research.elamur.ai/free/en/universities/sun-yat-sen-university/) — University, Guangzhou, China · 53 works · 9,185 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works

🧭 Topics (1): [🗨️ Dialogue systems](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) · 624 works

## Cite

Cite as: Tianjian Liu, Fanqi Wan, Jiajian Guo and Xiaojun Quan. 2026. ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents. 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{liu2026proactiveeval,
  title = {{ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents}},
  author = {Tianjian Liu, Fanqi Wan, Jiajian Guo and Xiaojun Quan},
  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.1906},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1906}
}
```

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*Updated 31 October 2026*