Your LLM Agents are Temporally Blind: The Misalignment Between Tool Use Decisions and Human Time Perception
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915939075751936 |
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| author | Cheng, Yize Moakhar, Arshia Soltani Fan, Chenrui Hosseini, Parsa Faghih, Kazem Sodagar, Zahra Wang, Wenxiao Feizi, Soheil |
| author_facet | Cheng, Yize Moakhar, Arshia Soltani Fan, Chenrui Hosseini, Parsa Faghih, Kazem Sodagar, Zahra Wang, Wenxiao Feizi, Soheil |
| contents | Large language model (LLM) agents are increasingly used to interact with and execute tasks in dynamic environments. However, a critical yet overlooked limitation of these agents is that they, by default, assume a stationary context, failing to account for the real-world time elapsed between messages. We refer to this as "temporal blindness". This limitation hinders decisions about when to invoke tools, leading agents to either over-rely on stale context and skip needed tool calls, or under-rely on it and redundantly repeat tool calls. To study this challenge, we constructed TicToc, a diverse dataset of multi-turn user-agent message trajectories across 76 scenarios, spanning dynamic environments with high, medium, and low time sensitivity. We collected human preferences between "calling a tool" and "directly answering" on each sample, and evaluated how well LLM tool-calling decisions align with human preferences under varying amounts of elapsed time. Our analysis reveals that existing models display poor alignment with human temporal perception, with no model achieving a normalized alignment rate better than 65% when given time stamp information. We also show that naive, prompt-based alignment techniques have limited effectiveness for most models, but specific post-training alignment can be a viable way to align multi-turn LLM tool use with human temporal perception. Our data and findings provide a first step toward understanding and mitigating temporal blindness, offering insights to foster the development of more time-aware and human-aligned agents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23853 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Your LLM Agents are Temporally Blind: The Misalignment Between Tool Use Decisions and Human Time Perception Cheng, Yize Moakhar, Arshia Soltani Fan, Chenrui Hosseini, Parsa Faghih, Kazem Sodagar, Zahra Wang, Wenxiao Feizi, Soheil Computation and Language Large language model (LLM) agents are increasingly used to interact with and execute tasks in dynamic environments. However, a critical yet overlooked limitation of these agents is that they, by default, assume a stationary context, failing to account for the real-world time elapsed between messages. We refer to this as "temporal blindness". This limitation hinders decisions about when to invoke tools, leading agents to either over-rely on stale context and skip needed tool calls, or under-rely on it and redundantly repeat tool calls. To study this challenge, we constructed TicToc, a diverse dataset of multi-turn user-agent message trajectories across 76 scenarios, spanning dynamic environments with high, medium, and low time sensitivity. We collected human preferences between "calling a tool" and "directly answering" on each sample, and evaluated how well LLM tool-calling decisions align with human preferences under varying amounts of elapsed time. Our analysis reveals that existing models display poor alignment with human temporal perception, with no model achieving a normalized alignment rate better than 65% when given time stamp information. We also show that naive, prompt-based alignment techniques have limited effectiveness for most models, but specific post-training alignment can be a viable way to align multi-turn LLM tool use with human temporal perception. Our data and findings provide a first step toward understanding and mitigating temporal blindness, offering insights to foster the development of more time-aware and human-aligned agents. |
| title | Your LLM Agents are Temporally Blind: The Misalignment Between Tool Use Decisions and Human Time Perception |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.23853 |