# Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models

arXiv: https://arxiv.org/abs/2504.05258 · HTML: https://mlsanity.com/paper/2504.05258/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 9 of its 39 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Placed in

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

## Builds on (most cited first, 9 of 9)

- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [s1: Simple test-time scaling](https://mlsanity.com/paper/2501.19393/) (2025)
- [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://mlsanity.com/paper/2112.00114/) (2021)
- [Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback](https://mlsanity.com/paper/2302.12813/) (2023)
- [Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning](https://mlsanity.com/paper/2406.09170/) (2024)
- [SelfEvolve: A Code Evolution Framework via Large Language Models](https://mlsanity.com/paper/2306.02907/) (2023)
- [MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models](https://mlsanity.com/paper/2310.05157/) (2023)
- [Unlocking Temporal Question Answering for Large Language Models with Tailor-Made Reasoning Logic](https://mlsanity.com/paper/2305.15014/) (2023)

## Built on by (most cited first, 10 of 10)

- [REMem: Reasoning with Episodic Memory in Language Agent](https://mlsanity.com/paper/2602.13530/) (2026)
- [It's High Time: A Survey of Temporal Question Answering](https://mlsanity.com/paper/2505.20243/) (2025)
- [Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2510.13975/) (2025)
- [Efficient Temporal-aware Matryoshka Adaptation for Temporal Information Retrieval](https://mlsanity.com/paper/2601.05549/) (2026)
- [GTR-Bench: Evaluating Geo-Temporal Reasoning in Vision-Language Models](https://mlsanity.com/paper/2510.07791/) (2025)
- [NeSTR: A Neuro-Symbolic Abductive Framework for Temporal Reasoning in Large Language Models](https://mlsanity.com/paper/2512.07218/) (2025)
- [Strategic Reflectivism In Intelligent Systems](https://mlsanity.com/paper/2505.22987/) (2025)
- [AdapTime: Enabling Adaptive Temporal Reasoning in Large Language Models](https://mlsanity.com/paper/2604.24175/) (2026)
- [Reflection or Re-Generation? Why LLM Revision Fails Where Human Revision Succeeds](https://mlsanity.com/paper/2607.28908/) (2026)
- [Evaluating Multi-Dimensional Generalization of Large Language Models in Temporal Extraction Tasks](https://mlsanity.com/paper/2610.02549/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have emerged as powerful tools for generating coherent text, understanding context, and performing reasoning tasks. However, they struggle with temporal reasoning, which requires processing time-related information such as event sequencing, durations, and inter-temporal relationships. These capabilities are critical for applications including question answering, scheduling, and historical analysis. In this paper, we introduce TISER, a novel framework that enhances the temporal reasoning abilities of LLMs through a multi-stage process that combines timeline construction with iterative self-reflection. Our approach leverages test-time scaling to extend the length of reasoning traces, enabling models to capture complex temporal dependencies more effectively. This strategy not only boosts reasoning accuracy but also improves the traceability of the inference process. Experimental results demonstrate state-of-the-art performance across multiple benchmarks, including out-of-distribution test sets, and reveal that TISER enables smaller open-source models to surpass larger closed-weight models on challenging temporal reasoning tasks.