# Temporal Knowledge Question Answering via Abstract Reasoning Induction

arXiv: https://arxiv.org/abs/2311.09149 · HTML: https://mlsanity.com/paper/2311.09149/

## Key facts

- Cited by 23 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 4 of its 53 references that are in the map.
- First posted on arXiv 15 Nov 2023.

## Placed in

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

## Builds on (most cited first, 4 of 4)

- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [Learning From Mistakes Makes LLM Better Reasoner](https://mlsanity.com/paper/2310.20689/) (2023)
- [Large Language Models Are Semi-Parametric Reinforcement Learning Agents](https://mlsanity.com/paper/2306.07929/) (2023)
- [LMEye: An Interactive Perception Network for Large Language Models](https://mlsanity.com/paper/2305.03701/) (2023)

## Built on by (most cited first, 23 of 23)

- [Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models](https://mlsanity.com/paper/2402.16568/) (2024)
- [Temporal Knowledge Graph Question Answering: A Survey](https://mlsanity.com/paper/2406.14191/) (2024)
- [DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoning](https://mlsanity.com/paper/2507.13396/) (2025)
- [When Silence Is Golden: Can LLMs Learn to Abstain in Temporal QA and Beyond?](https://mlsanity.com/paper/2602.04755/) (2026)
- [A Review of Incorporating Psychological Theories in LLMs](https://mlsanity.com/paper/2505.00003/) (2025)
- [Efficient and Transferable Agentic Knowledge Graph RAG via Reinforcement Learning](https://mlsanity.com/paper/2509.26383/) (2025)
- [Temp-R1: A Unified Autonomous Agent for Complex Temporal KGQA via Reverse Curriculum Reinforcement Learning](https://mlsanity.com/paper/2601.18296/) (2026)
- [A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation](https://mlsanity.com/paper/2402.13587/) (2024)
- [Combining LLMs and Knowledge Graphs to Reduce Hallucinations in Question Answering](https://mlsanity.com/paper/2409.04181/) (2024)
- [Reading Between the Timelines: RAG for Answering Diachronic Questions](https://mlsanity.com/paper/2507.22917/) (2025)
- [Cognitive Visual-Language Mapper: Advancing Multimodal Comprehension with Enhanced Visual Knowledge Alignment](https://mlsanity.com/paper/2402.13561/) (2024)
- [On the Temporal Question-Answering Capabilities of Large Language Models Over Anonymized Data](https://mlsanity.com/paper/2504.07646/) (2025)
- [Modeling the Diachronic Evolution of Legal Norms: An LRMoo-Based, Component-Level, Event-Centric Approach to Legal Knowledge Graphs](https://mlsanity.com/paper/2506.07853/) (2025)
- [Plan of Knowledge: Retrieval-Augmented Large Language Models for Temporal Knowledge Graph Question Answering](https://mlsanity.com/paper/2511.04072/) (2025)
- [Large Language Models Lack Temporal Awareness of Medical Knowledge](https://mlsanity.com/paper/2605.13045/) (2026)
- [Does the Generator Mind its Contexts? An Analysis of Generative Model Faithfulness under Context Transfer](https://mlsanity.com/paper/2402.14488/) (2024)
- [A Question Answering Dataset for Temporal-Sensitive Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.12282/) (2025)
- [Panning for Gold: Expanding Domain-Specific Knowledge Graphs with General Knowledge](https://mlsanity.com/paper/2601.10485/) (2026)
- [Let the Agent Search: Autonomous Exploration Beats Rigid Workflows in Temporal Question Answering](https://mlsanity.com/paper/2603.01853/) (2026)
- [Strategy-Induct: Task-Level Strategy Induction for Instruction Generation](https://mlsanity.com/paper/2605.20924/) (2026)
- [PulseCX: Breaking the Closed-World Assumption in Real-Time CX](https://mlsanity.com/paper/2606.21124/) (2026)
- [SABET-QA: Temporal Knowledge Graph Question Answering](https://mlsanity.com/paper/2608.20083/) (2026)
- [SCoP: Structured Constraint Parsing for Evidence-Space Control in Temporal Knowledge Graph Question Answering](https://mlsanity.com/paper/2609.22213/) (2026)

## Abstract (arXiv)

In this study, we address the challenge of enhancing temporal knowledge reasoning in Large Language Models (LLMs). LLMs often struggle with this task, leading to the generation of inaccurate or misleading responses. This issue mainly arises from their limited ability to handle evolving factual knowledge and complex temporal logic. To overcome these limitations, we propose Abstract Reasoning Induction (ARI) framework, which divides temporal reasoning into two distinct phases: Knowledge-agnostic and Knowledge-based. This framework offers factual knowledge support to LLMs while minimizing the incorporation of extraneous noisy data. Concurrently, informed by the principles of constructivism, ARI provides LLMs the capability to engage in proactive, self-directed learning from both correct and incorrect historical reasoning samples. By teaching LLMs to actively construct knowledge and methods, it can significantly boosting their temporal reasoning abilities. Our approach achieves remarkable improvements, with relative gains of 29.7% and 9.27% on two temporal QA datasets, underscoring its efficacy in advancing temporal reasoning in LLMs. The code can be found at https://github.com/czy1999/ARI-QA