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New Time Puzzles benchmark reveals LLMs struggle with iterative temporal reasoning

By PulseAugur Editorial · [1 sources] ·

A new benchmark called Time Puzzles has been developed to evaluate the iterative temporal reasoning capabilities of large language models, particularly when using tools like web search. The benchmark, introduced by researchers including Zhengxiang Wang, found that even advanced models like GPT-5 struggle with this type of reasoning, achieving only 55.3% accuracy without tools. While web search improves performance, models perform significantly better when temporal constraints are explicitly stated with dates, highlighting a gap in reliable tool use for complex temporal tasks. AI

IMPACT Highlights a gap in LLM tool use for temporal reasoning, potentially guiding future model development and evaluation.

RANK_REASON The cluster contains a new academic paper introducing a novel benchmark for evaluating LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Time Puzzles benchmark reveals LLMs struggle with iterative temporal reasoning

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The cluster contains a new academic paper introducing a novel benchmark for evaluating LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zhengxiang Wang, Zeyu Dong ·

    Measuring Iterative Temporal Reasoning with Time Puzzles

    arXiv:2601.07148v4 Announce Type: replace-cross Abstract: Tool use, such as web search, has become a standard capability even in freely available large language models (LLMs). However, existing benchmarks evaluate temporal reasoning mainly in static, non-tool-using settings, whic…

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