Real-Time Deadlines Reveal Temporal Awareness Failures in LLM Strategic Dialogues
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| Format: | Preprint |
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2026
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| _version_ | 1866911386432438272 |
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| author | Sehgal, Neil K. R. Guntuku, Sharath Chandra Ungar, Lyle |
| author_facet | Sehgal, Neil K. R. Guntuku, Sharath Chandra Ungar, Lyle |
| contents | Large Language Models (LLMs) generate text token-by-token in discrete time, yet real-world communication, from therapy sessions to business negotiations, critically depends on continuous time constraints. Current LLM architectures and evaluation protocols rarely test for temporal awareness under real-time deadlines. We use simulated negotiations between paired agents under strict deadlines to investigate how LLMs adjust their behavior in time-sensitive settings. In a control condition, agents know only the global time limit. In a time-aware condition, they receive remaining-time updates at each turn. Deal closure rates are substantially higher (32\% vs. 4\% for GPT-5.1) and offer acceptances are sixfold higher in the time-aware condition than in the control, suggesting LLMs struggle to internally track elapsed time. However, the same LLMs achieve near-perfect deal closure rates ($\geq$95\%) under turn-based limits, revealing the failure is in temporal tracking rather than strategic reasoning. These effects replicate across negotiation scenarios and models, illustrating a systematic lack of LLM time awareness that will constrain LLM deployment in many time-sensitive applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_13206 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Real-Time Deadlines Reveal Temporal Awareness Failures in LLM Strategic Dialogues Sehgal, Neil K. R. Guntuku, Sharath Chandra Ungar, Lyle Artificial Intelligence Large Language Models (LLMs) generate text token-by-token in discrete time, yet real-world communication, from therapy sessions to business negotiations, critically depends on continuous time constraints. Current LLM architectures and evaluation protocols rarely test for temporal awareness under real-time deadlines. We use simulated negotiations between paired agents under strict deadlines to investigate how LLMs adjust their behavior in time-sensitive settings. In a control condition, agents know only the global time limit. In a time-aware condition, they receive remaining-time updates at each turn. Deal closure rates are substantially higher (32\% vs. 4\% for GPT-5.1) and offer acceptances are sixfold higher in the time-aware condition than in the control, suggesting LLMs struggle to internally track elapsed time. However, the same LLMs achieve near-perfect deal closure rates ($\geq$95\%) under turn-based limits, revealing the failure is in temporal tracking rather than strategic reasoning. These effects replicate across negotiation scenarios and models, illustrating a systematic lack of LLM time awareness that will constrain LLM deployment in many time-sensitive applications. |
| title | Real-Time Deadlines Reveal Temporal Awareness Failures in LLM Strategic Dialogues |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.13206 |