dorsal/arxiv
View SchemaIs More Context Always Better? Examining LLM Reasoning Capability for Time Interval Prediction
| Authors | Yanan Cao, Farnaz Fallahi, Murali Mohana Krishna Dandu, Lalitesh Morishetti, Kai Zhao, Luyi Ma, Sinduja Subramaniam, Jianpeng Xu, Evren Korpeoglu, Kaushiki Nag, Sushant Kumar, Kannan Achan |
|---|---|
| Categories | |
| ArXiv ID | 2601.10132vv1 |
| URL | https://arxiv.org/abs/2601.10132 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
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.
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"abstract": "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\u0027s LLMs in structured temporal inference and offers guidance for designing future context-aware hybrid models that integrate statistical precision with linguistic flexibility.",
"arxiv_id": "2601.10132",
"authors": [
"Yanan Cao",
"Farnaz Fallahi",
"Murali Mohana Krishna Dandu",
"Lalitesh Morishetti",
"Kai Zhao",
"Luyi Ma",
"Sinduja Subramaniam",
"Jianpeng Xu",
"Evren Korpeoglu",
"Kaushiki Nag",
"Sushant Kumar",
"Kannan Achan"
],
"categories": [
"cs.AI",
"cs.LG"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Is More Context Always Better? Examining LLM Reasoning Capability for Time Interval Prediction",
"url": "https://arxiv.org/abs/2601.10132",
"version": "v1"
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