# Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties

arXiv: https://arxiv.org/abs/2502.16922 · HTML: https://mlsanity.com/paper/2502.16922/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Builds on (most cited first, 10 of 10)

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- [WebWalker: Benchmarking LLMs in Web Traversal](https://mlsanity.com/paper/2501.07572/) (2025)
- [ChronoSense: Exploring Temporal Understanding in Large Language Models with Time Intervals of Events](https://mlsanity.com/paper/2501.03040/) (2025)
- [Cultural Palette: Pluralising Culture Alignment via Multi-agent Palette](https://mlsanity.com/paper/2412.11167/) (2024)
- [CKnowEdit: A New Chinese Knowledge Editing Dataset for Linguistics, Facts, and Logic Error Correction in LLMs](https://mlsanity.com/paper/2409.05806/) (2024)
- [BaiJia: A Large-Scale Role-Playing Agent Corpus of Chinese Historical Characters](https://mlsanity.com/paper/2412.20024/) (2024)

## Abstract (arXiv)

Temporal reasoning is fundamental to human cognition and is crucial for various real-world applications. While recent advances in Large Language Models have demonstrated promising capabilities in temporal reasoning, existing benchmarks primarily rely on rule-based construction, lack contextual depth, and involve a limited range of temporal entities. To address these limitations, we introduce Chinese Time Reasoning (CTM), a benchmark designed to evaluate LLMs on temporal reasoning within the extensive scope of Chinese dynastic chronology. CTM emphasizes cross-entity relationships, pairwise temporal alignment, and contextualized and culturally-grounded reasoning, providing a comprehensive evaluation. Extensive experimental results reveal the challenges posed by CTM and highlight potential avenues for improvement.