arXiv:2510.23853v1 Announce Type: new 
Abstract: Large language model agents are increasingly used in multi-turn conversational settings to interact with and execute tasks in dynamic environments. However, a key limitation is their temporal blindness: they, by default, operate with a stationary context, failing to account for the real-world time elapsed between messages. This becomes a critical liability when an agent must decide whether to invoke a tool based on how much time has passed since the last observation. Without temporal awareness, agents often either over-rely on previous context (skipping necessary tool calls), or under-rely on it (unnecessarily repeating tool calls). To study this challenge, we introduce TicToc-v1, a test set of multi-turn user-agent trajectories across 34 scenarios with varying time sensitivity. Each trajectory ends with a user question, where the need for a tool call depends on the amount of time elapsed since the last message. To give LLMs temporal context, we augment dialogue messages with explicit timestamps, bridging the gap between static dialogue and evolving environments. We then collected human preferences for these samples, creating two subsets: one where humans preferred relying on the previous observation (prefer-noTool), and another where they preferred a new tool call (prefer-Tool). We evaluated how well LLM tool-calling decisions align with human preferences under varying time intervals on TicToc-v1. Our analysis show that without time information, most models perform only slightly better than random, with the top alignment rate being just over 60%. While adding timestamps leads to a slight improvement, particularly for larger models, the improvement is modest, peaking at around 65%. We also show that naive, prompt-based alignment have limited effectiveness. Our findings highlight the need for specific post-training alignment to align multi-turn LLM tool use with human temporal perception.

تسلط دراسة حديثة الضوء على مشكلة العمى الزمني في نماذج اللغة الكبيرة المستخدمة في المحادثات متعددة الأدوار. غالبًا ما تفشل هذه النماذج في أخذ الوقت الحقيقي الذي يمر بين الرسائل في الاعتبار، مما قد يعيق قدرتها على استخدام الأدوات بشكل فعال. هذه الأبحاث مهمة لأنها تسلط الضوء على قيد كبير في تفاعلات الذكاء الاصطناعي، مما يبرز الحاجة إلى تحسينات في كيفية إدراك هذه النماذج للوقت واستجابتها له، مما يعزز في النهاية وظيفتها في البيئات الديناميكية.

Un estudio reciente destaca el problema de la ceguera temporal en los agentes de modelos de lenguaje grande utilizados en conversaciones de múltiples turnos. Estos agentes a menudo no consideran el tiempo real que transcurre entre los mensajes, lo que puede obstaculizar su capacidad para utilizar herramientas de manera efectiva. Esta investigación es importante ya que arroja luz sobre una limitación significativa en las interacciones de IA, enfatizando la necesidad de mejoras en cómo estos modelos perciben y responden al tiempo, mejorando en última instancia su funcionalidad en entornos dinámicos.

Une étude récente met en lumière le problème de la cécité temporelle chez les agents de modèles de langage de grande taille utilisés dans des conversations multi-tours. Ces agents ne tiennent souvent pas compte du temps réel qui passe entre les messages, ce qui peut entraver leur capacité à utiliser efficacement des outils. Cette recherche est importante car elle souligne une limitation significative dans les interactions avec l'IA, mettant en avant la nécessité d'améliorations dans la façon dont ces modèles perçoivent et réagissent au temps, améliorant ainsi leur fonctionnalité dans des environnements dynamiques.

A recent study highlights the issue of temporal blindness in large language model agents used in multi-turn conversations. These agents often fail to consider the real-world time that passes between messages, which can hinder their ability to effectively use tools. This research is important as it sheds light on a significant limitation in AI interactions, emphasizing the need for improvements in how these models perceive and respond to time, ultimately enhancing their functionality in dynamic environments.

Temporal Blindness in Multi-Turn LLM Agents: Misaligned Tool Use vs. Human Time Perception

arXiv:2510.21060v2 Announce Type: replace 
Abstract: Policy optimization (PO) is a cornerstone of modern reinforcement learning (RL), with diverse applications spanning robotics, healthcare, and large language model training. The increasing deployment of PO in sensitive domains, however, raises significant privacy concerns. In this paper, we initiate a theoretical study of differentially private policy optimization, focusing explicitly on its sample complexity. We first formalize an appropriate definition of differential privacy (DP) tailored to PO, addressing the inherent challenges arising from on-policy learning dynamics and the subtlety involved in defining the unit of privacy. We then systematically analyze the sample complexity of widely-used PO algorithms, including policy gradient (PG), natural policy gradient (NPG) and more, under DP constraints and various settings, via a unified framework. Our theoretical results demonstrate that privacy costs can often manifest as lower-order terms in the sample complexity, while also highlighting subtle yet important observations in private PO settings. These offer valuable practical insights for privacy-preserving PO algorithms.

تم نشر دراسة حديثة حول تحسين السياسات بشكل مختلف (DPPO) تركز على تعقيد العينة في تحسين السياسات (PO) في التعلم المعزز (RL). تتناول هذه الدراسة المخاوف المتعلقة بالخصوصية في التطبيقات الحساسة مثل الروبوتات والرعاية الصحية من خلال صياغة تعريف للخصوصية المختلفة يتناسب مع PO وتحليل تعقيد العينة لعدة خوارزميات PO تحت قيود DP.

Se ha publicado un estudio reciente sobre la optimización de políticas diferencialmente privadas (DPPO), centrado en la complejidad de muestra de la optimización de políticas (PO) en el aprendizaje por refuerzo (RL). Esta investigación aborda las preocupaciones de privacidad en aplicaciones sensibles como la robótica y la atención médica al formalizar una definición de privacidad diferencial adaptada a la PO y analizar la complejidad de muestra de varios algoritmos de PO bajo restricciones de DP.

Une étude récente sur l'optimisation de politiques différemment privées (DPPO) a été publiée, se concentrant sur la complexité d'échantillonnage de l'optimisation de politiques (PO) dans l'apprentissage par renforcement (RL). Cette recherche aborde les préoccupations en matière de confidentialité dans des applications sensibles telles que la robotique et les soins de santé en formalisant une définition de la confidentialité différentielle adaptée à la PO et en analysant la complexité d'échantillonnage de divers algorithmes de PO sous des contraintes de DP.

A recent study on differentially private policy optimization (DPPO) has been published, focusing on the sample complexity of policy optimization (PO) in reinforcement learning (RL). This research addresses privacy concerns in sensitive applications such as robotics and healthcare by formalizing a definition of differential privacy tailored to PO and analyzing the sample complexity of various PO algorithms under DP constraints.

On the Sample Complexity of Differentially Private Policy Optimization

One More Thing in AI – Your Shortcut to AI Mastery

Temporal Blindness in Multi-Turn LLM Agents: Misaligned Tool Use vs. Human Time Perception

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