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

arXiv: https://arxiv.org/abs/2601.13206 · HTML: https://mlsanity.com/paper/2601.13206/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 19 Jan 2026.

## Builds on (most cited first, 9 of 9)

- [MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents](https://mlsanity.com/paper/2503.01935/) (2025)
- ["Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding](https://mlsanity.com/paper/1909.03065/) (2019)
- [How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis](https://mlsanity.com/paper/2402.05863/) (2024)
- [TIMEDIAL: Temporal Commonsense Reasoning in Dialog](https://mlsanity.com/paper/2106.04571/) (2021)
- [Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method](https://mlsanity.com/paper/2402.15813/) (2024)
- [Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues](https://mlsanity.com/paper/2402.13550/) (2024)
- [Kani: A Lightweight and Highly Hackable Framework for Building Language Model Applications](https://mlsanity.com/paper/2309.05542/) (2023)
- [Leveraging Large Language Models for Active Merchant Non-player Characters](https://mlsanity.com/paper/2412.11189/) (2024)
- [Discrete Minds in a Continuous World: Do Language Models Know Time Passes?](https://mlsanity.com/paper/2506.05790/) (2025)

## Built on by (most cited first, 5 of 5)

- [Can LLMs Perceive Time? An Empirical Investigation](https://mlsanity.com/paper/2604.00010/) (2026)
- [ContractBench: Can LLM Agents Preserve Observation Contracts?](https://mlsanity.com/paper/2605.17281/) (2026)
- [Temporal Preference Concepts and their Functions in a Large Language Model](https://mlsanity.com/paper/2606.05194/) (2026)
- [Evaluating Investment Logic in Large Language Models: A Real-World Benchmark Towards Personalzied Financial Agents](https://mlsanity.com/paper/2608.06108/) (2026)
- [AgentTime: Can Agents Estimate and Control Their Own Runtime?](https://mlsanity.com/paper/2610.09944/) (2026)

## Abstract (arXiv)

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 negotiations between paired agents under strict deadlines to study adaptation to real-time pressure. Agents either receive only the initial deadline or explicit remaining-time updates at each turn. Remaining-time feedback raises deal closure from 4% to 32% for GPT-5.1-chat-latest and increases offer acceptance more than sixfold. The same model achieves near-perfect closure under turn-based limits, showing that poor wall-clock performance is not simply due to insufficient negotiation competence. Across additional interface conditions, qualitative urgency cues can outperform numeric countdowns, repeated deadline reminders do not consistently reproduce their benefits, and directed time tracking can help or hurt depending on the model. Across additional negotiation scenarios and model configurations, we find real-time temporal adaptation is fragile, model-dependent, and sensitive to how temporal constraints are presented. Code available at https://github.com/sehgal-neil/llm-temporal-awareness