---
title: "Benchmarking Temporal Reasoning and Alignment Across"
description: "They offer little narrative context and test few entities at a time. 24 references. Among them: 0."
authors: ["Zhenglin Wang", "Jialong Wu", "Pengfei Li", "Yong Jiang", "Deyu Zhou"]
year: 2026
venue: "Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)"
source: "https://aclanthology.org/2026.eacl-short.6/"
doi: "10.18653/v1/2026.eacl-short.6"
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/wang-2026-benchmarking-temporal-reasoning-alignment-across/"
---
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# Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties

**Authors:** [Zhenglin Wang](https://md.research.elamur.ai/free/en/authors/zhenglin-wang/), [Jialong Wu](https://md.research.elamur.ai/free/en/authors/jialong-wu/), [Pengfei Li](https://md.research.elamur.ai/free/en/authors/pengfei-li/), [Yong Jiang](https://md.research.elamur.ai/free/en/authors/yong-jiang/) and [Deyu Zhou](https://md.research.elamur.ai/free/en/authors/deyu-zhou/) · **Year:** 2026 · **Venue:** [Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2026](https://md.research.elamur.ai/free/en/venues/eacl/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.eacl-short.6/) · [PDF](https://aclanthology.org/2026.eacl-short.6.pdf) · [DOI 10.18653/v1/2026.eacl-short.6](https://doi.org/10.18653/v1/2026.eacl-short.6) · [OpenAlex](https://openalex.org/W7140145249)

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

## Summary
Existing temporal reasoning benchmarks for LLMs are mostly English and rule-generated. They offer little narrative context and test few entities at a time. The authors address this with Chinese Time Reasoning (CTM), a Chinese-language benchmark based on dynastic chronology from roughly 2100 BCE to 1912. It draws on a curated repository of over 4,700 cultural entities, including historical figures, places, allusions, food ingredients and intangible heritage items, collected from sources such as CBDB and CHGIS. Questions were generated with LLMs and then validated.

CTM has two parts:
- **Question answering:** tasks such as dynasty identification, plausibility checks, spotting anachronisms in film-style scripts, and interval calculation.
- **Timeline Ito Game:** a cooperative multi-agent game in which agents must order entities in time using only thematic hints.

Twelve LLMs were tested with direct prompting and chain-of-thought. All of them struggle, especially with precise date arithmetic and with temporal alignment in the game. The benchmark therefore exposes clear gaps in culturally grounded temporal knowledge.

## Key points
- **Size and sources:** CTM contains 8,750 QA pairs and 60 Timeline Ito Game instances. The underlying repository holds 1,652 figures, 2,907 places, 93 allusions, 49 ingredients and 44 heritage items, drawn from Gushiwen, CBDB, CHGIS, Wikipedia and Ihchina. History is grouped into ten dynastic periods.
- **QA tasks:** Nine task types make up the QA portion: EDD, PJ, TOU and RR, along with SEC and LSEC, plus EEU, TIC and TES.
- **Evaluation setup:** QA accuracy is judged by GPT-4o, and the game is scored with Pass@3 and Pass@8.
- **QA results:** Qwen-max with CoT has the best QA average (about 57%). TIC is the hardest task, with scores of roughly 20% or lower. Accuracy falls as more entities are involved and as the dynastic gap between entities shrinks. CoT helps most models but hurts on long-script correction.
- **Game results:** GPT-4o stays below 40 on Pass@8, and models under 32B never exceed 10. Adding Google top-10 search snippets (open-book setting) gives modest gains for most models but not for Qwen2.5-7B.

*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: Zhenglin Wang, Jialong Wu, Pengfei Li, Yong Jiang and Deyu Zhou. 2026. Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers).

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

```bibtex
@inproceedings{wang2026benchmarking,
  title = {{Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties}},
  author = {Zhenglin Wang, Jialong Wu, Pengfei Li, Yong Jiang and Deyu Zhou},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-short.6},
  url = {https://doi.org/10.18653/v1/2026.eacl-short.6}
}
```

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*Updated 1 November 2026*