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LLMs show fragile real-time deadline adaptation in strategic dialogues

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv reveals that large language models (LLMs) exhibit fragile temporal adaptation, struggling with real-time deadlines in strategic dialogues. Researchers found that providing explicit remaining-time updates significantly improved deal closure rates for GPT-5.1-chat-latest, increasing it from 4% to 32%. The study also indicated that qualitative urgency cues can be more effective than numeric countdowns, and the model's performance is highly dependent on how temporal constraints are presented. AI

IMPACT Highlights a key limitation in LLM strategic reasoning, suggesting improvements are needed for real-world time-sensitive applications.

RANK_REASON Research paper published on arXiv detailing 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 →

LLMs show fragile real-time deadline adaptation in strategic dialogues

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27 / 100
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Research paper published on arXiv detailing 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) · Neil K. R. Sehgal, Sharath Chandra Guntuku, Lyle Ungar ·

    Real-Time Deadlines Reveal Fragile Temporal Adaptation in LLM Strategic Dialogues

    arXiv:2601.13206v2 Announce Type: replace Abstract: 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. We use simulated negoti…

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