# Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning? arXiv: https://arxiv.org/abs/2406.09072 ยท HTML: https://mlsanity.com/paper/2406.09072/ ## Key facts - Cited by 25 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 25 of its 54 references that are in the map. - First posted on arXiv 13 Jun 2024. ## Placed in - LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/) ## Builds on (most cited first, 25 of 25) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023) - [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023) - [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023) - [GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints](https://mlsanity.com/paper/2305.13245/) (2023) - [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023) - [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023) - [Multimodal Chain-of-Thought Reasoning in Language Models](https://mlsanity.com/paper/2302.00923/) (2023) - [WizardCoder: Empowering Code Large Language Models with Evol-Instruct](https://mlsanity.com/paper/2306.08568/) (2023) - [PAL: Program-aided Language Models](https://mlsanity.com/paper/2211.10435/) (2022) - [WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct](https://mlsanity.com/paper/2308.09583/) (2023) - [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022) - [RealTime QA: What's the Answer Right Now?](https://mlsanity.com/paper/2207.13332/) (2022) - [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022) - [Can AI Assistants Know What They Don't Know?](https://mlsanity.com/paper/2401.13275/) (2024) - [TRAM: Benchmarking Temporal Reasoning for Large Language Models](https://mlsanity.com/paper/2310.00835/) (2023) - [TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models](https://mlsanity.com/paper/2311.17667/) (2023) - [RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering](https://mlsanity.com/paper/2210.14353/) (2022) - [MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models](https://mlsanity.com/paper/2310.05157/) (2023) - [Question Answering as Programming for Solving Time-Sensitive Questions](https://mlsanity.com/paper/2305.14221/) (2023) - [GLoRE: Evaluating Logical Reasoning of Large Language Models](https://mlsanity.com/paper/2310.09107/) (2023) - [Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition](https://mlsanity.com/paper/2204.05544/) (2022) - [Unlocking Temporal Question Answering for Large Language Models with Tailor-Made Reasoning Logic](https://mlsanity.com/paper/2305.15014/) (2023) - [Joint Multi-Facts Reasoning Network For Complex Temporal Question Answering Over Knowledge Graph](https://mlsanity.com/paper/2401.02212/) (2024) - [Back to the Future: Towards Explainable Temporal Reasoning with Large Language Models](https://mlsanity.com/paper/2310.01074/) (2023) ## Built on by (most cited first, 25 of 25) - [OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement Learning](https://mlsanity.com/paper/2505.08617/) (2025) - [A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond](https://mlsanity.com/paper/2503.21614/) (2025) - [LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training](https://mlsanity.com/paper/2406.16554/) (2024) - [Twin-Merging: Dynamic Integration of Modular Expertise in Model Merging](https://mlsanity.com/paper/2406.15479/) (2024) - [PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Models](https://mlsanity.com/paper/2501.03124/) (2025) - [Timo: Towards Better Temporal Reasoning for Language Models](https://mlsanity.com/paper/2406.14192/) (2024) - [Large Language Model Reasoning Failures](https://mlsanity.com/paper/2602.06176/) (2026) - [UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs' Memorization](https://mlsanity.com/paper/2407.03525/) (2024) - [It's High Time: A Survey of Temporal Question Answering](https://mlsanity.com/paper/2505.20243/) (2025) - [SURf: Teaching Large Vision-Language Models to Selectively Utilize Retrieved Information](https://mlsanity.com/paper/2409.14083/) (2024) - [Do Cognitively Interpretable Reasoning Traces Improve LLM Performance?](https://mlsanity.com/paper/2508.16695/) (2025) - [On Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusion](https://mlsanity.com/paper/2406.15480/) (2024) - [From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration](https://mlsanity.com/paper/2503.12821/) (2025) - [Interpretable Traces, Unexpected Outcomes: Investigating the Disconnect in Trace-Based Knowledge Distillation](https://mlsanity.com/paper/2505.13792/) (2025) - [ExAnte: A Benchmark for Ex-Ante Inference in Large Language Models](https://mlsanity.com/paper/2505.19533/) (2025) - [Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications](https://mlsanity.com/paper/2507.00914/) (2025) - [Mitigating Multilingual Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2408.00550/) (2024) - [TRAVELER: A Benchmark for Evaluating Temporal Reasoning across Vague, Implicit and Explicit References](https://mlsanity.com/paper/2505.01325/) (2025) - [How GPT learns layer by layer](https://mlsanity.com/paper/2501.07108/) (2025) - [Factual Knowledge in Language Models: Robustness and Anomalies under Simple Temporal Context Variations](https://mlsanity.com/paper/2502.01220/) (2025) - [AgentCaster: Reasoning-Guided Tornado Forecasting](https://mlsanity.com/paper/2510.03349/) (2025) - [RAG-Enhanced Large Language Models for Dynamic Content Expiration Prediction in Web Search](https://mlsanity.com/paper/2605.13052/) (2026) - [MIN-Merging: Merge the Important Neurons for Model Merging](https://mlsanity.com/paper/2510.17890/) (2025) - [Teaching Large Language Models When Not to Know: Learning Temporal Critique for Ex-Ante Reasoning](https://mlsanity.com/paper/2605.14636/) (2026) - [The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes](https://mlsanity.com/paper/2606.11470/) (2026) ## Abstract (arXiv) Temporal reasoning is fundamental for large language models (LLMs) to comprehend the world. Current temporal reasoning datasets are limited to questions about single or isolated events, falling short in mirroring the realistic temporal characteristics involving concurrent nature and intricate temporal interconnections. In this paper, we introduce CoTempQA, a comprehensive co-temporal Question Answering (QA) benchmark containing four co-temporal scenarios (Equal, Overlap, During, Mix) with 4,748 samples for evaluating the co-temporal comprehension and reasoning abilities of LLMs. Our extensive experiments reveal a significant gap between the performance of current LLMs and human-level reasoning on CoTempQA tasks. Even when enhanced with Chain of Thought (CoT) methodologies, models consistently struggle with our task. In our preliminary exploration, we discovered that mathematical reasoning plays a significant role in handling co-temporal events and proposed a strategy to boost LLMs' co-temporal reasoning from a mathematical perspective. We hope that our CoTempQA datasets will encourage further advancements in improving the co-temporal reasoning capabilities of LLMs. Our code is available at https://github.com/zhaochen0110/Cotempqa.