# Is More Context Always Better? Examining LLM Reasoning Capability for Time Interval Prediction

arXiv: https://arxiv.org/abs/2601.10132 · HTML: https://mlsanity.com/paper/2601.10132/

## Key facts

- Cited by 4 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 8 of its 13 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Placed in

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

## Builds on (most cited first, 8 of 8)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [Large Language Models Are Zero-Shot Time Series Forecasters](https://mlsanity.com/paper/2310.07820/) (2023)
- [LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting](https://mlsanity.com/paper/2402.16132/) (2024)
- [Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilities](https://mlsanity.com/paper/2402.10835/) (2024)
- [Not Just What, But When: Integrating Irregular Intervals to LLM for Sequential Recommendation](https://mlsanity.com/paper/2507.23209/) (2025)
- [S2SRec2: Set-to-Set Recommendation for Basket Completion with Recipe](https://mlsanity.com/paper/2507.09101/) (2025)

## Built on by (most cited first, 4 of 4)

- [SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning](https://mlsanity.com/paper/2602.19455/) (2026)
- [Beyond Ranking Accuracy: Evaluating LLM-Cited Feature Rationales for Next Basket Repurchase Recommendation](https://mlsanity.com/paper/2608.30333/) (2026)
- [The average-farmer illusion in language-model simulations of agricultural decisions](https://mlsanity.com/paper/2609.15038/) (2026)
- [Can LLMs Access their World Knowledge for Event Prediction?](https://mlsanity.com/paper/2610.19021/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning and prediction across different domains. Yet, their ability to infer temporal regularities from structured behavioral data remains underexplored. This paper presents a systematic study investigating whether LLMs can predict time intervals between recurring user actions, such as repeated purchases, and how different levels of contextual information shape their predictive behavior. Using a simple but representative repurchase scenario, we benchmark state-of-the-art LLMs in zero-shot settings against both statistical and machine-learning models. Two key findings emerge. First, while LLMs surpass lightweight statistical baselines, they consistently underperform dedicated machine-learning models, showing their limited ability to capture quantitative temporal structure. Second, although moderate context can improve LLM accuracy, adding further user-level detail degrades performance. These results challenge the assumption that "more context leads to better reasoning". Our study highlights fundamental limitations of today's LLMs in structured temporal inference and offers guidance for designing future context-aware hybrid models that integrate statistical precision with linguistic flexibility.